Machine Learning Serving

Machine Learning Serving Developer Jobs

18 jobs found

Today

Senior Machine Learning Engineer - Automation for Translation and Multilingual Intelligence

Apple Cupertino, CA

Cupertino, CA

Today

$150k - $278k/yr Full time Ai Ml Artificial Intelligence Data Pipeline

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Job Description

Join Apple’s AI/ML Translation & Multilingual Intelligence team and help build scalable model automation that supports training, evaluation, deployment, and ML CI/CD for translation and related NLP workloads. This onsite role in Cupertino focuses on practical tooling and pipeline performance, partnering closely with software engineering and QE to move models into production.

What you’ll do

Design and develop automation pipelines and tools for training, evaluating, and deploying machine learning models for machine translation and related NLP tasks
Implement and optimize ML pipelines with a focus on distributed data processing, training scalability, and strong inference throughput and efficiency
Collaborate with software engineers and QE to integrate ML models into production systems

What you bring

3+ years of experience across the ML lifecycle, including model management, data processing, and large distributed systems and cloud computing (for example: Spark, Ray, Dask)
Strong Python coding skills and experience with ML infrastructure tools such as vLLM, SGLang, TRT-llm, Slime, or Megatron-LM
Excellent communication and problem-solving skills

Technology areas you’ll work with

You’ll be working across the ML stack for translation and multilingual intelligence, including Python, PyTorch, TensorFlow, JAX, transformer architectures, NLP, MT, and ASR, along with distributed data processing frameworks such as Spark, Ray, and Dask. Model and inference tooling may include vLLM, SGLang, TRT-llm, Slime, and Megatron-LM.

Benefits at Apple

Comprehensive medical and dental coverage
Retirement benefits
A range of discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary stock programs and Apple shareholder participation through discretionary employee stock programs
Discretionary restricted stock unit awards
Apple stock purchase at a discount when voluntarily participating in the Employee Stock Purchase Plan
This role might be eligible for discretionary bonuses or commission payments, as well as relocation

Pay & eligibility notes

The base pay range for this role is $150,400 to $277,600 per year. Base pay depends on skills, qualifications, experience, and location. Apple employees are eligible for discretionary restricted stock unit awards. Apple stock purchase at a discount is available through voluntary participation in the Employee Stock Purchase Plan. Benefit, compensation, and employee stock program availability are subject to eligibility requirements and other terms of the applicable plan or program.

Preferred qualifications

Strong machine learning expertise with hands-on experience in model fine-tuning, evaluation, experiment tracking, pipeline building, and deployment
Experience with LLMs and neural machine translation
Deep knowledge of ML frameworks and technologies such as NLP, MT, ASR, PyTorch, TensorFlow, JAX, and transformer architectures

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1d ago

Sr. Machine Learning Engineer

Workday Boulder, CO

Boulder, CO

1d ago

$163k - $257k/yr Full time Ai Ml Artificial Intelligence Automation

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Job Description

Workday is hiring a Senior Machine Learning Engineer for the AI Model Serving team, responsible for technical leadership on a production platform that powers Workday agents and large-scale machine learning workloads. The role focuses on designing and operating high-throughput systems, improving reliability and performance, and supporting governance and cost controls for AI model serving.

Key Responsibilities

Provide technical leadership by making critical design decisions that improve platform performance, reliability, and scalability.
Design, implement, and maintain large-scale systems that move machine learning models into production.
Author design documents to align teams on new system components and enhancements to existing components.
Evaluate and adopt new technologies available within Workday and across the broader industry.
Troubleshoot, improve, and scale continuous integration software pipelines.
Collaborate with software engineers, machine learning engineers, and data scientists across partner teams.
Respond to alerts and debug production issues to maintain platform health and reliability.
Review pull requests with focus on consistency, performance, readability, and security.
Develop documentation to share knowledge across the engineering organization.

Required Qualifications

6+ years of related experience in software development, with emphasis on building and operating large-scale distributed systems.
Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
Deep hands-on experience deploying and scaling workloads on Kubernetes, with a focus on GPU resource management.
Understanding of optimizing GPU utilization for hosting and tuning smaller open-weight LLMs using modern inference engines (for example, vLLM and SGLang).
Familiarity with GPU memory constraints, serving tuned models such as LoRA, and autoscaling based on hardware metrics.
Deep experience designing, building, and scaling production-grade distributed systems.
Working knowledge of the full software development lifecycle, including coding standards, testing, code reviews, source control, and deployment for complex, high-throughput platforms.
Strong proficiency in Python, including production-level code and building systems using Python-based frameworks.
Familiarity with both large language models and traditional ML models, including serving, scaling, and production monitoring.
Ability to understand operational differences across model types and design abstractions that support both effectively.
Experience designing and maintaining monitoring strategies that provide visibility into system health, performance, and cost.
Excellent written and verbal communication skills, including producing clear design documents and articulating complex technical ideas to build consensus.
Collaborative mindset, including experience mentoring engineers and fostering an inclusive team environment.

Technologies

Ray Serve
Kubernetes
GPU
vLLM
SGLang
LoRA
Python
Continuous integration

About the Team

The AI Model Serving team is the engine behind every production Workday agent and machine learning use case.
Owns services that power all production AI workloads.
Acts as the gateway to vendor-hosted LLMs including GCP, AWS Bedrock, and Gemini, while also serving and scaling Workday-hosted internal models.
Hosts thousands of traditional ML models across sharded Ray Serve clusters and maintains Workday’s production model registry.
Supports approximately 2,000 requests per second, with peaks above 10,000 RPS in the largest clusters.
Scaling architecture supports upgrading systems to support 20+ new AI agents as they enter production.
Open-weight LLM hosting includes infrastructure for hosting and tuning open-source LLMs within Workday’s stack.
Focuses on performance and reliability by driving down core latency while maintaining stability.
Implements enterprise governance by hardening a unified vendor interface and adding advanced cost-governance controls.

About the Role

As a Senior Machine Learning Engineer on the AI Model Serving team, you will shape the platform’s vision and direction while driving outcomes through critical design decisions. Your work will influence Workday’s ability to serve AI at scale, covering traditional ML models through to the latest large language models that power Workday’s agents.

Location and Compensation

Location: Boulder, CO (hybrid)
Salary Range: USD 163,000 - 257,400 per year
Workday Pay Transparency Statement (Primary Location): USA.CO.Boulder, base pay range $171,600 - $257,400
Additional US Locations (Base Pay Range): $163,000 - $288,000
Total Compensation Note: May be eligible for a Workday Bonus Plan or role-specific commission/bonus and annual refresh stock grants

Flexible Work

With Flex Work, spend at least half (50%) of your time each quarter in the office or in the field with customers, prospects, and partners (depending on role).

Application Deadline

The application deadline for this role is the same as the posting end date: 10/30/2026.

Equal Opportunity and Accommodations

Workday considers qualified applicants with arrest and conviction records pursuant to applicable Fair Chance law.
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.
Reasonable accommodations are available during the application process.
If you need a reasonable accommodation, email accommodations@workday.com as far in advance as possible.

Workday Careers Policy

Workday will never ask candidates to apply to jobs through websites that are not Workday Careers.
Workday will never ask candidates to pay a recruiting fee or pay for consulting or coaching services to apply for a job.

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1d ago

Machine Learning Engineer Graduate (Conversational AI)

TikTok Seattle, WA

Seattle, WA

1d ago

$122k - $243k/yr Full time Agentic Ai Ai Agent Ai Ml

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Job Description

Machine Learning Engineer Graduate (Conversational AI) at TikTok (Seattle) for a 2027 start, building a self-evolving unified agent system for e-Commerce customer service across 30+ languages.

Responsibilities

Agent runtime (harness / agent loop): Orchestrate skills, tools, and context; implement loop control and intervention; use progressive disclosure and behavior-level guardrails.
Production safety layer: Build pre-flight budgets and timeout truncation; add serve-time gates; deliver shadow and swap-in answers; define safe fallback paths.
Context and memory for long multi-turn agents: Implement agentic memory via structured note-taking; perform context compaction/summarization; support context editing and observation masking; use retrieve-then-load (just-in-time) retrieval.
Post-training and the data flywheel: Apply SFT, DPO, and RL approaches to internalize rules into weights so the prompt gets shorter; distill to smaller serving models; convert served conversations into training, evaluation, and retrieval signals.
Tools, skills, and MCP: Develop tools-as-APIs and connectors; enable skill/tool search across large inventories; govern a skill library (description conflict handling, trigger evaluations, cross-skill mis-fire matrices); load skills on demand instead of dumping all definitions into context.
Evaluation you can bet a launch on: Use LLM-as-judge with human-agreement calibration; apply statistical rigor (paired comparison, confidence intervals, repeated sampling, pass^k); maintain held-out and time-rolling evaluation splits with overfitting alarms; use cascaded scoring and cross-family judge panels to reduce evaluation cost at scale.
Self-evolving loop: Run case mining → automatic root-cause → constrained candidate generation → replay verification against frozen regression sets → canary → data flywheel.
Auditable system plumbing: Maintain a candidate registry with exact runtime read-back, change lineage, and an archive of rejected candidates for sampling in later rounds.
Online experimentation and causal readout: Execute shadow/canary/A-B testing; enforce non-inferiority gates; monitor traffic-split health; define metrics that hold up under scrutiny; apply off-policy counterfactual evaluation when live A/B is not possible.

Requirements

Completed or recently completed a Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline.
Strong Python plus one of: C++, Go, Rust, or Java.
Solid ML/DL/NLP fundamentals with hands-on experience using LLMs or agents through coursework, research, internship, competition, open-source, or a serious side project.
Basic statistical literacy: compute confidence intervals, explain what a p-value does and does not mean, and distinguish “number went up” from “system got better.”
Ability to read a paper or engineering blog and implement working code.

Technologies

Python, C++, Go, Rust, Java
SFT, DPO, RLHF, RLAIF, RLVR
vLLM, TensorRT-LLM, MoE, KV
MCP
A/B, LLM-as-judge
ACM-ICPC, Kaggle

Preferred Qualifications

Built agent runtime functionality beyond calling APIs, even at research/hobby/competition scale (agent loop/harness, memory/context management, RAG or tool-use agent, or fine-tuned/post-trained model).
Hands-on depth in one of the following areas (breadth welcome, depth in one sufficient): post-training (SFT/DPO/RLHF/RLAIF/RLVR, reward modeling, reward hacking and defenses); agent systems (harness, context engineering, MCP/skills, sub-agents, tool search); evaluation/experimentation (LLM-as-judge and calibration, pass^k, regression suites, A/B and non-inferiority testing, off-policy evaluation); self-improving/evolutionary systems (evolutionary search, candidate archives/parent sampling, automatic prompt/context optimization, Pareto selection, credit assignment); inference/serving (vLLM/TensorRT-LLM, MoE, KV/prompt caching, cost engineering); multilingual NLP.
Publications (for PhD), strong competition outcomes (ACM-ICPC / Kaggle / ML competitions), or notable open-source contributions.
Taste for accuracy in metrics and significance, including willingness to communicate when results are not yet significant.
e-Commerce or multilingual experience is a plus (not required).

Benefits

Day one access to medical, dental, and vision insurance.
401(k) savings plan with company match.
Paid parental leave.
Short-term and long-term disability coverage.
Life insurance.
Wellbeing benefits.
10 paid holidays per year.
10 paid sick days per year.
17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

Compensation

Base salary range (Seattle): $121,600 - $243,200 per year.
Compensation may vary outside the range based on qualifications, skills, competencies, experience, and location.
Base pay is part of the Total Package and the role may be eligible for additional discretionary bonuses/incentives and restricted stock units.

Additional Application Constraints

Must be able to commit to an onboarding date by the end of the year.
State availability and graduation date clearly in the resume.
Apply to a maximum of two positions; applications are considered in the order submitted.
Application limit applies globally for the company and its affiliates.
Reviewed on a rolling basis; apply early.

Location and Role Details

Location: Seattle, WA (onsite).
Employment type: Regular.
Job code: A31120.

Team Introduction

Build a next-generation unified Agent system for TikTok’s global e-Commerce customer service, running in 30+ languages.
North star is a self-evolving agent where post-training, harness, memory/context engineering, tools, and evaluation form a closed loop and each served conversation becomes the next iteration’s training/eval/retrieval/skill-induction signal.
Evaluation and experimentation are treated as first-class systems.
Develop the agent runtime (not prompts on top of a vendor API).
Work includes generative recommendation, large recommendation models, multimodal representation learning, and cross-domain value modeling.

Accommodation and Fair Chance Notices

TikTok provides reasonable accommodations in recruitment for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. Request support at https://tinyurl.com/RA-request.
For Los Angeles County (Unincorporated) candidates: qualified applicants with arrest or conviction records will be considered in accordance with applicable fair chance laws; the company notes that criminal history may have a direct adverse relationship on duties including interacting with internal/external clients/colleagues (occasionally unsupervised), handling confidential information and access to IT systems, and exercising sound judgment.

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2d ago

Machine Learning Engineer - E-Commerce Recommendation Live

TikTok Seattle, WA

Seattle, WA

2d ago

$207k - $368k/yr Full time Data Pipeline Generative Ai Engineer Generative Recommendations

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Job Description

Join TikTok in Seattle to build the end-to-end recommendation stack powering TikTok Shop LIVE.

Responsibilities

Scale Large Recommendation Models (LRM): Push scaling for a unified ranking foundation model, including model capacity, sparse MoE, ultra-long user sequences, and pre-training with teacher-student distillation.
Convert offline scaling gains into online wins under strict latency and compute budgets using dynamic compute allocation and on-device real-time inference.
Generative recommendation on multimodal Semantic IDs: Design semantic IDs that fuse visual, speech, text, collaborative, and product signals (including what is being showcased, pinned, or auctioned in a live room).
Power generative retrieval with near-real-time SID serving.
LLM4Rec and MLLM cross-domain modeling: Use multimodal LLMs to represent live rooms, short videos, and products in one semantic space.
Inject world knowledge to transfer user interests across content, search, and shopping, and address cold start for new creators, new products, and new markets.
Reward models and post-training for recommenders: Build reward models for conversion, long-term value, and user experience; align recommenders with SFT and DPO/GRPO-style RL post-training.
Apply generator-evaluator decoding and listwise objectives for training and optimization.
Agent x Recommendation: Build LLM agents to auto-tune multi-objective value weights and traffic mechanisms, design and read experiments, and diagnose online cases.
Move from hand-tuned heuristics toward agentic optimization and multi-step RL.
Long-term value and ecosystem: Model LTV, uplift, and multi-touch attribution; design value and traffic mechanisms for live auctions, brands, and emerging creators.
Balance short-term GMV with user experience and a healthy, growing creator ecosystem.
Own it end to end: Drive work from problem framing and data pipelines through large-scale training, online serving, A/B testing, and launch; partner with engineering, product, and data science.

Requirements

Education: Bachelor’s degree or above in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
Experience: 3+ years in recommendation, search, advertising, or other large-scale applied machine learning with a record of shipped models that improved online metrics.
ML depth: Strong machine learning fundamentals and hands-on experience in at least one area such as retrieval and ranking, sequential user modeling, multi-task learning, generative recommendation, or LLM/multimodal modeling.
Programming + frameworks: Proficiency in Python and/or C++, plus solid experience with PyTorch or TensorFlow, including distributed training and model optimization.
Execution: Ability to turn ambiguous business problems into modeling solutions and own iteration from offline experiments to online A/B results.

Technologies

Python
C++
PyTorch
TensorFlow

Benefits

Day one access to medical, dental, and vision insurance
401(k) savings plan with company match
Paid parental leave
Short-term and long-term disability coverage
Life insurance
Wellbeing benefits
10 paid holidays per year
10 paid sick days per year
17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
Additional discretionary bonuses/incentives (eligible may apply)
Restricted stock units (eligible may apply)

About the Team

Live commerce is described as the hardest recommendation problem at TikTok.
The Global E-commerce Recommendation Live Algorithm team owns the end-to-end recommendation stack for TikTok Shop LIVE, from retrieval and pre-ranking to ranking, mixed ranking, and long-term value modeling.
A unified stack serves seven core surfaces, covering public discovery and private follower relationships.
The team is rebuilding the stack around foundation models, including:
- Large recommendation models following scaling laws
- Generative retrieval on multimodal semantic IDs
- Multimodal LLMs connecting content and commerce
- Reward models and RL post-training aligned to long-term value
- AI agents that tune, debug, and evolve the system
What the team ships directly drives GMV, buyer growth, and creator success for TikTok Shop in the US and globally.

Location

Seattle, WA (onsite)

Compensation

USD 207,480 - 368,220 per year
Base salary range in the selected city: $207480 - $368220 annually
Employees may be eligible for additional discretionary bonuses/incentives and restricted stock units
Benefits may vary depending on the nature of employment and the country work location

Job Details

Job code: A254886
For Pay Transparency

Preferred Qualifications

Experience in e-commerce or live-streaming recommendation, ads ranking, or other real-time conversion-driven systems
Hands-on experience with large recommendation models or generative recommendation, including scaling laws, MoE, long-sequence modeling, pre-training, or distillation
Experience in LLM/MLLM post-training (SFT, reward modeling, RLHF, DPO, GRPO, or other RL-based alignment), ideally for recommendation or ranking
Experience building LLM agents (tool use, planning, automated experimentation, or parameter tuning) or applying reinforcement learning to multi-step decision-making like traffic allocation
Experience with cross-domain transfer, LTV/long-term value modeling, uplift modeling and causal inference, or multi-objective optimization
Experience with large-scale training and inference efficiency (mixed precision, model parallelism, GPU serving optimization)
Publications at top venues such as NeurIPS, ICML, ICLR, KDD, SIGIR, WWW, RecSys, ACL, or CVPR, or strong results in major ML competitions

Accommodation

TikTok provides reasonable accommodations in recruitment for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other protected reasons.
Request assistance here: https://tinyurl.com/RA-request

About TikTok

TikTok is described as the leading destination for short-form mobile video.
Global headquarters: Los Angeles and Singapore.
Additional offices: New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Los Angeles County (Unincorporated) Fair Chance

Qualified applicants with arrest or conviction records will be considered in accordance with federal, state, and local laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Criminal history may have a direct, adverse, and negative relationship on certain job duties and could result in withdrawal of a conditional offer.
Role may involve interacting and occasionally having unsupervised contact with internal/external clients or colleagues.
Role requires appropriately handling confidential information and access to IT systems.
Requires exercising sound judgment.

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2d ago

Software Engineer, Systems ML

Meta Menlo Park, CA

Menlo Park, CA

2d ago

$122k - $181k/yr Full time Engineering High Performance Computing Machine Learning Engineering

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Job Description

Meta is seeking a Software Engineer for the Systems ML Engineering team to design and optimize large-scale machine learning infrastructure. The role covers the end-to-end lifecycle of model training and inference pipelines, including hardware-aware performance optimization and production support.

Responsibilities

Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelines
Develop and maintain high-performance ML infrastructure components in C++ and Python, with emphasis on reliability, scalability, and low-latency execution
Use profiling, instrumentation, and benchmarking tools to identify and resolve performance bottlenecks across the ML stack
Architect and evaluate ML system design trade-offs, including memory bandwidth, compute utilization, and I/O throughput
Partner with research and product teams to translate model requirements into efficient infrastructure solutions
Write automated tests for expected behaviors, failure modes, and error paths for ML infrastructure components
Build monitoring and alerting to detect production anomalies and support safe operation of ML systems
Contribute to staged rollout strategies using feature flagging and experimentation frameworks to deploy ML system changes safely
Produce accurate technical documentation when introducing new ML infrastructure features or updating existing systems
Participate in on-call rotations to investigate and mitigate production incidents affecting ML serving systems, and contribute detailed retrospectives
Provide constructive code review feedback and contribute to engineering programs that improve the health and quality of the ML infrastructure codebase

Requirements

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience; the degree must be completed prior to joining Meta
2+ years of experience in software engineering with a focus on machine learning systems, AI infrastructure, or high-performance computing
Experience developing or optimizing ML training or inference pipelines using frameworks such as PyTorch, TensorFlow, or equivalent
Experience with distributed computing architectures and large-scale systems design for ML workloads
Experience programming in C++ and Python for performance-critical systems
Experience using profiling and performance analysis tools to identify and resolve bottlenecks in ML or compute-intensive systems

Technologies

C++
Python
PyTorch
TensorFlow

Preferred Qualifications

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques
Experience optimizing large-scale ranking or recommendation model inference on AI accelerator hardware such as GPUs or TPUs

Role Location and Compensation

Location: Menlo Park, CA (onsite)

Salary: USD 121,992 to 181,000 per year

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3d ago

Software Engineer, ML Serving Platform

DoorDash USA San Francisco, CA

San Francisco, CA

3d ago

$131k - $192k/yr Full time Backend Developer Engineering GPU

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Job Description

DoorDash is hiring a Software Engineer to build a self-serve ML Serving Platform connecting request routing and online feature retrieval to model inference on CPU and GPU infrastructure (onsite).

Responsibilities

Build next-generation ML serving infrastructure that ties together request routing, online feature retrieval, and model inference on CPU and GPU
Address infrastructure challenges across latency, reliability, resource efficiency, and scale
Develop self-serve tools and workflows to make serving capabilities easier for internal teams to use
Drive defined projects end to end: technical design, implementation, testing, rollout, and production support
Partner with internal teams to understand evolving modeling requirements, reduce adoption barriers, and deliver measurable improvements from open source advances
Build tools, APIs, and workflows that enable teams to deploy, configure, validate, and operate models independently
Evaluate and integrate evolving inference frameworks and enable open source models, turning promising capabilities into reliable, efficient services at scale
Improve systems that route prediction requests, retrieve online features, and execute models to support tens of millions of predictions per second
Create modular components so routing, feature retrieval, and model execution can evolve and scale independently as workloads and modeling requirements change
Ensure workloads respond to changing traffic while meeting latency and availability goals and using CPU/GPU resources efficiently
Build integrations, validation, and migration tooling to help teams adopt new serving capabilities and a unified global platform with confidence
Use benchmarking, profiling, metrics, and tracing to find bottlenecks; participate in on-call and improve automation and runbooks

Requirements

2+ years of software engineering experience building and maintaining production services or infrastructure
Strong computer science fundamentals and proficiency in a backend or systems language: Java, Kotlin, Go, C++, or Python
Knowledge of distributed systems fundamentals: concurrency, networking, timeouts, failure handling, and performance trade-offs
Ability to independently turn a defined problem into technical design, tested implementation, and a safe production rollout
Experience debugging production systems and using operational data to improve reliability, performance, or cost
Care about the engineers using your platform; collaborate effectively with internal partners to help complex infrastructure adoption
Degree in Computer Science or related field, or equivalent practical experience

Technologies

Java, Kotlin, Go, C++, Python
Kubernetes
CPU, GPU

Nice to Have

Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services
Experience operating containerized workloads on Kubernetes, including deployment, resource management, or autoscaling
Experience building self-serve developer platforms, APIs, or automation that helps other engineers move faster
Experience integrating open source infrastructure or models and validating behavior under production workloads
Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows

Location: San Francisco, CA (onsite)
Compensation: USD 130,600 - 192,000 per year
Experience: 2+ years
Education: Degree in Computer Science or related field (or equivalent practical experience)

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3d ago

Machine Learning Engineer

Solventum Pittsburgh, PA 🇺🇸 Remote

Pittsburgh, PA

3d ago

$106k - $146k/yr Full time Artificial Intelligence Data Analysis Data Analytics

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Job Description

In this role, you will build and maintain the machine learning infrastructure that supports production AI within Solventum’s Healthcare Information Systems (HIS). You will connect data science and software engineering through automated workflows, cloud operations, secure service delivery, and practical monitoring.

Role Overview

As an ML Engineer, you will ensure reliable model performance in real-world healthcare workflows. The position emphasizes production-grade pipelines, scalable deployment patterns for clinical use, and disciplined handling of regulated healthcare data. Work will include CI/CD for machine learning, ETL and dataset preparation using healthcare standards (FHIR, HL7), and integration with core backend systems.

Responsibilities

Build and maintain CI/CD pipelines for machine learning, including automated testing, model deployment, and version control (for example, using MLflow and Git).
Deploy machine learning models as scalable APIs and microservices, meeting performance and latency needs for clinical use.
Implement monitoring to track model performance, data drift, and overall system health in production.
Develop and optimize ETL processes that transform healthcare data (FHIR, HL7) into clean, usable datasets for training and inference.
Help build and maintain feature stores and data layers to ensure consistency between training and production environments.
Collaborate with backend teams to integrate ML outputs into Solventum’s core healthcare applications.
Write clean, maintainable, and well-documented Python code.
Participate in code reviews to support reliability across systems.
Use Docker and Kubernetes to package and orchestrate machine learning workloads across environments.
Follow established HIPAA and HITRUST security protocols for data handling and deployments.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or Data Engineering.
4 years of professional experience in software engineering or data engineering, including at least 2 years focused on machine learning production environments.
2 years of experience with Python and SQL.
Knowledge of a compiled language (such as Go or Java).
2 years of hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and Docker containerization.
2 years of experience with ML libraries (PyTorch or Scikit-learn) and MLOps tools (Airflow, Prefect, BentoML, or Kubeflow).
2 years of experience with data processing frameworks (Pandas, Spark, or dbt).

Technologies and Tools

MLflow, Git, Python, SQL
Go, Java
AWS, Azure, GCP
Docker, Kubernetes
PyTorch, Scikit-learn
Airflow, Prefect, BentoML, Kubeflow
Pandas, Spark, dbt
FHIR, HL7, APIs, microservices
HIPAA, HITRUST

Helpful Additional Qualifications

Familiarity with deploying Large Language Models (LLMs) or using frameworks such as LangChain.
Experience working in a regulated environment (Healthcare, Finance, or similar).
Understanding of API design and microservices architecture.

Location and Work Model

Location: Pittsburgh, PA (remote)

Travel: May include up to 10% domestic

Compensation

The expected compensation range for this position is $106,000 - $145,750 per year, including base pay plus variable incentive pay, if eligible.

Onboarding and Travel for New Hires

New employees hired for this position will be required to travel to a designated company location for on-site onboarding during their initial days of employment. Travel arrangements and related expenses will be coordinated and paid for by the company in accordance with its travel policy. This applies to new hires with a start date of October 1, 2025 or later.

Application Integrity Notice

Solventum is committed to maintaining high standards of integrity and professionalism in its recruitment process. Applicants must remain alert to fraudulent job postings and recruitment schemes that falsely claim to represent Solventum and seek to exploit job seekers.

Please note that all email communications from Solventum regarding job opportunities with the company will be from an email with an appropriate Solventum domain. Be wary of unsolicited emails or messages regarding Solventum job opportunities from emails with other email domains.