Machine Learning Platform

Machine Learning Platform Developer Jobs

12 jobs found

Today

Principal Machine Learning Engineer

Amgen Remote 🇺🇸 Remote

Remote

Today

$187k - $254k/yr Full time Ai Ml Artificial Intelligence Cloud Machine Learning

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Job Description

Amgen is seeking a Principal Machine Learning Engineer for a remote role in the U.S. This is a senior individual-contributor position centered on building and scaling enterprise AI/ML and generative-AI capabilities. The role focuses on owning AI/ML architecture, deploying and monitoring models (including LLMs), and partnering across engineering, security, compliance, DevOps, and product to deliver production-ready solutions that are secure and cost-effective.

Key Responsibilities

Own enterprise AI/ML architecture, including standards, APIs, and guardrails across cloud and on-prem environments.
Build production ML and GenAI solutions, including lightweight applications designed to deliver sub-second insights.
Develop end-to-end ML pipelines covering data ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration, and automated promotion using Kubeflow, SageMaker Pipelines, Open AI SDK, or equivalent MLOps stacks.
Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines, or business-logic layers to ensure insights reach users with sub-second latency.
Establish observability and SLOs, implement safe deployment strategies (blue-green/canary, shadow, rollbacks), and maintain incident runbooks.
Lead rigorous model evaluation (offline/online, A/B), drive drift detection, and automate retraining.
Architect LLM/RAG solutions with prompt management, safety guardrails, and optimized inference.
Enforce data quality, lineage, and model/data cards, applying privacy-preserving techniques when needed.
Contribute reusable ML/GenAI components such as feature stores, model registries, and experiment-tracking libraries, and share best practices to improve engineering velocity.
Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to support algorithm selection and ensure robustness.
Prototype and benchmark new algorithms, advising on scalability trade-offs and production readiness while co-owning model-performance KPIs.
Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps, mentor teams, and communicate technical trade-offs.

Requirements

Education and experience combination: Doctorate degree + 2 years of Machine Learning Engineer experience OR Master’s degree + 6 years OR Bachelor’s degree + 8 years OR Associate’s degree + 10 years OR High school diploma/GED + 12 years.
At least 2 years of experience directly managing people and/or leadership experience leading teams, projects, programs, or directing allocation of resources.
3-5 years in AI/ML and enterprise software.
Strong command of machine-learning algorithms (regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures including CNNs, RNNs, transformers) and modern LLM/RAG techniques, with the judgment to select, tune, and operationalize approaches.
Proven ability selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
Expertise with GenAI tooling including vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks such as LangChain, LangGraph, and Semantic Kernel.
Proficiency in Python and Java, containerization (Docker/Kubernetes), cloud (AWS, Azure, or GCP), and modern DevOps/MLOps such as GitHub Actions and Bedrock/SageMaker Pipelines.
Strong business-case skills, including ability to model TCO vs. NPV and present trade-offs to executives.
Exceptional stakeholder management, with ability to translate complex technical concepts into concise, outcome-oriented narratives.

Technologies

Kubeflow, SageMaker Pipelines, Open AI SDK
Python, Java
Docker, Kubernetes (K8s)
AWS, Azure, GCP
GitHub Actions, Bedrock
Vector databases, LangChain, LangGraph, Semantic Kernel
LLM/RAG, prompt management
Blue-green/canary, shadow, rollbacks

Benefits

Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
Group medical, dental and vision coverage
Life and disability insurance
Flexible spending accounts
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible

Preferred Qualifications

Experience in biotechnology or the pharma industry
Published thought-leadership or conference talks on enterprise GenAI adoption
Master’s degree in Computer Science and or Data Science
Familiarity with Agile methodologies and Scaled Agile Framework (SAFe)

Education and Professional Certifications

Master’s degree with 10-12+ years of experience in Computer Science, IT or related field
Bachelor’s degree with 12-14+ years of experience in Computer Science, IT or related field
Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus

Additional Details

Location: Remote (remote)
Salary: USD 187,395 - 253,534 per year
Application deadline: Amgen does not have an application deadline for this position and will continue accepting applications until sufficient candidates are received or a selection is made
Sponsorship: Sponsorship for this role is not guaranteed

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Today

Principal Machine Learning Engineer

HubSpot Cambridge, MA

Cambridge, MA

Today

$286k - $457k/yr Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

HubSpot is seeking a Principal Machine Learning Engineer in Cambridge, MA (onsite) to build foundational AI infrastructure that enables agentic product experiences. The role will focus on core platform capabilities such as agent runtime, evaluation systems, quality measurement, and model optimization to improve evaluation quality, product experience, and scalability across the organization.

Key Responsibilities

Build infrastructure spanning agent runtime, evaluation, quality measurement, and model optimization.
Create tooling that helps teams interpret evaluation strengths, identify missing coverage, and detect user question types the system is not yet prepared to handle.
Design signal pipelines that surface user frustration, agent failure states, and quality issues before they materialize as CSAT declines.
Help define a HubSpot-specific AI benchmark to evaluate model performance on real HubSpot workloads.
Develop systems to evaluate new models across agents, improving product quality, reducing cost, and supporting future model routing.
Make fine-tuning and task-specific optimization more repeatable for HubSpot’s AI use cases.

Required Qualifications

Deep experience with production ML, LLM, or AI infrastructure.
Experience building large-scale systems where reliability, latency, quality, and cost are all important considerations.
Ability to think in platforms rather than one-off implementations.
Capability to collaborate across infrastructure, product, backend, and ML teams.
Experience in one or more of the following areas: evaluation frameworks, model serving, fine-tuning, signal extraction, experimentation, feedback loops, or model optimization.
Interest in ambiguous, foundational problems where many aspects still need to be defined.
Ability to influence senior engineers and leaders through technical credibility and clear judgment.

About the Team

HubSpot is building foundational AI infrastructure that powers its next generation of agentic product experiences. This team owns Breeze Engine, the platform behind Breeze Assistant and other AI-powered product experiences, and Aviator, HubSpot’s internal framework for building and running agents. This is a high-scope infrastructure role focused on leverage across the company, rather than a feature-team assignment.

Location

Cambridge, MA (onsite)

Compensation

USD 285,800 - 457,300 per year

Work Arrangement and In-Person Requirements

Remote employees and office-based employees are required to attend a regional HubSpot office for in-person onboarding if they join the Engineering team.
If you join HubSpot’s broader Product team, you will attend other in-person events, including a Product Group Summit and additional gatherings.
If you require an accommodation due to travel limitations or other reasons, you should inform your recruiter during the hiring process so alternative arrangements can be discussed.

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2d ago

Staff Machine Learning Engineer

Integral Ad Science, Inc. New York, NY 🇺🇸 Remote

New York, NY

2d ago

$135k - $232k/yr Full time Ai Ml Artificial Intelligence Data Science

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Job Description

Integral Ad Science, Inc. is seeking a Staff Machine Learning Engineer to join the Data Science team in New York, NY (remote). This role focuses on technical leadership for large-scale AI/ML services and production machine learning systems that support globally deployed models across open web, social, video, and mobile apps. If you enjoy shaping end-to-end architecture, mentoring engineering talent, and turning ambitious ideas into production outcomes, this is a strong fit.

What you’ll do

Set the architectural vision and drive system design for core AI/ML-based services.
Serve as the technical lead for complex, multi-quarter initiatives from inception through global deployment.
Design and build deep learning infrastructure and platforms for distributed model training, with an emphasis on low-latency and high availability at enterprise scale.
Lead architecture design reviews by establishing standard methodologies and defining code quality expectations.
Collaborate with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy.
Translate abstract product requirements into concrete technical designs and production-ready implementations.
Mentor and guide senior and mid-level data scientists and engineers.
Work with large-scale AI training infrastructure and ecosystems, including accelerators, network fabrics, CUDA, NCCL, RDMA, and big data tooling such as Databricks, Spark, Kubernetes, Kafka, and Prometheus.
Design, develop, and support robust CI/CD pipelines so ML models move reliably from research into dependable production endpoints.

What you bring

PhD/Master’s in a technical field such as computer science, mathematics, statistics, or equivalent years of experience.
7+ years of industry machine learning experience, with a track record operating at the Lead, Principal, or Staff level.
Deep expertise designing complex ML systems, including ranking infrastructures and data pipelines capable of massive throughput.
Production-hardened experience with frameworks including PyTorch, JAX, and/or TensorFlow.
Strong production-grade programming skills in Python, Go, Java, or C++, plus solid software design principles and algorithms.
Proven ability to operate in ambiguity, take large unstructured problems, and drive them to successful technical resolution.
A strong history of collaborating with, elevating, and mentoring engineering talent.

Compensation and benefits

Salary range: USD 135,100 - 231,600 per year.

Paid time off
Health insurance (medical, dental, vision)
PPO, HSA and FSA options
401k with employer matching contributions
Annual bonus and/or other incentive plans

Tools you’ll work with

PyTorch, JAX, TensorFlow, Python, Go, Java, C++, CUDA, NCCL, RDMA, Databricks, Spark, Kubernetes, Kafka, Prometheus

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3d ago

Staff Machine Learning Engineer

Coupang Mountain View, CA

Mountain View, CA

3d ago

$164k - $282k/yr Full time Marketing Ad Tech Ai Ml

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Job Description

Build next-generation machine learning systems for Coupang’s offsite advertising business in Mountain View.

Responsibilities

Design and build ML models and systems to improve ad targeting, ranking, bidding, and campaign performance across Coupang Offsite Ads.
Develop large-scale prediction and optimization models for CTR, CVR, GMV, and bidding optimization.
Apply modern AI approaches including LLMs, embeddings, agentic AI, and deep learning to address complex advertising challenges.
Build scalable ML infrastructure and production systems to support rapid experimentation and reliable model deployment.
Define success metrics, evaluate business impact, and improve model performance through data-driven experimentation.
Partner with Product, Engineering, Data Science, and business teams to solve high-impact customer and advertiser problems.
Drive initiatives from concept through production with strong ownership and accountability for business outcomes.
Mentor engineers and contribute to a culture focused on technical excellence, innovation, and continuous learning.

Requirements

Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related quantitative field.
6+ years of industry experience developing and deploying machine learning solutions in production environments.
Strong foundation in machine learning, deep learning, statistical modeling, optimization, and large-scale data systems.
Hands-on experience building production-grade ML systems using Python and/or Java.
Strong experience with PyTorch and modern deep learning architectures for prediction, ranking, recommendation, retrieval, or optimization problems.
Demonstrated ability to solve complex and ambiguous business problems through data-driven analysis and ML innovation.
Proven track record of owning large technical initiatives and delivering measurable business impact.

Technologies

Python, Java, PyTorch
LLMs, embeddings, agentic AI, deep learning
CTR, CVR, GMV
XGBoost, LightGBM, Scikit-learn
MLflow, Kubeflow, Spark, Airflow

Preferred Qualifications

Master’s degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
Experience in digital advertising, recommendation systems, ranking systems, marketplace optimization, or large-scale machine learning.
Experience with modern AI technologies including LLMs, generative AI, embedding models, retrieval systems, or agentic AI.
Strong expertise in PyTorch and developing large-scale production machine learning systems.
Experience with distributed training, model serving, and ML platforms in cloud environments.
Familiarity with ML tools and frameworks including PyTorch, XGBoost, LightGBM, Scikit-learn, MLflow, Kubeflow, Spark, and Airflow.
Demonstrated ability to solve ambiguous problems, drive technical strategy, and deliver measurable business impact.
Strong communication and collaboration skills to influence stakeholders across multiple functions.
Comfort operating in a fast-paced, high-growth environment with a strong sense of ownership and customer focus.

Pay & Benefits

Base salary: $164,000/year to $282,000/year (Staff position range).
Pay is based on factors including market location and may vary based on job-related knowledge, skills, and experience.
Annual bonus: 0-20% of base salary.
Medical/Dental/Vision/Life, AD&D insurance.
Flexible Spending Accounts (FSA) & Health Savings Account (HSA).
Long-term/Short-term Disability.
Employee Assistance Program (EAP) program.
401K Plan with Company Match.
Paid Time Off (PTO): 18-21 days/year based on tenure.
12 Public Holidays.
6 weeks Paid Parental leave.
Pre-tax commuter benefits.
MTV: [Free] Electric Car Charging Station.

Other Compensation

“Other Compensation” includes, but is not limited to, bonuses, equity, or other forms of compensation that may be offered in addition to the established salary range or wage scale.

Recruitment Process

Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer
The exact process may vary and can change due to scheduling or other circumstances.
Interview schedules and results are communicated via the email address submitted during application.

Additional Notes

This job posting may close prior to the stated application end date if openings are filled.
Coupang may rescind an offer if false information was submitted during the application process.
Candidates eligible for employment protection (for example, recipients of veteran’s benefits or the disabled) may receive preferential treatment in accordance with applicable laws.

Privacy Notice

Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice: https://www.coupang.jobs/privacy-policy/ . For reasonable accommodation during the recruiting process due to a disability, contact usrecruiting@coupang.com.

Requisition ID: R0078849

Location: Mountain View, CA (onsite)

Summary focus: Prediction, bidding optimization, auction dynamics modeling, generative retrieval, foundation models/LLM-powered advertising, and agentic AI systems for Offsite Ads.

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3d ago

Machine Learning Engineer

General Dynamics Information Technology McLean, VA

McLean, VA

3d ago

$168k - $228k/yr Full time AI Ai Ml Analytics

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Job Description

General Dynamics Information Technology (GDIT) is seeking an Artificial Intelligence and Machine Learning Engineer to help develop and adapt Large Language Models (LLMs) and Natural Language Processing (NLP) methodologies. This McLean, VA onsite role focuses on improving analytic workflows and supporting key intelligence questions through hands-on model development, tuning, and evaluation in secure environments.

In this position, you will work with modern deep learning frameworks, GPU-accelerated computing, and a variety of AI/ML tooling to build capabilities for data-driven solutions. You will also collaborate on implementation details across model development, ensembles, application-oriented use cases, and clear communication of modeling decisions to technical and non-technical audiences.

Responsibilities

Develop and adapt Large Language Models (LLM) and Natural Language Processing (NLP) methodologies using AI/ML frameworks and programming languages to create new capabilities for analytic workflows and intelligence question support.
Apply hands-on GPU experience for accelerated computing.
Tune neural networks, including LLMs, on custom datasets and apply models to specific use cases.
Build AI/ML models and ensembles, select appropriate Python libraries, prepare data, analyze results, and evaluate model performance.

Requirements

Bachelor or Master’s Degree in an IT discipline, or the equivalent combination of education, technical certifications or training, or work experience.
8+ years of experience.
Professional or academic experience with modern deep learning frameworks such as PyTorch, TensorFlow, or Keras (key experience).
Hands-on experience using GPUs for accelerated computing (key experience).
Experience tuning neural networks (including LLMs) on custom datasets and applying them to specific use cases.
Experience building AI/ML models and ensembles, selecting Python libraries, preparing data, performing analyses, and evaluating model performance.
Proficiency in Python or R.
Solid SQL skills, including use of common table expressions, set operations, aggregated functions, and nested subqueries.
Experience with version control tools such as GitHub and Jenkins.
Security Clearance Level: TS/SCI with Polygraph.
Location: McLean, VA, On Customer Site.
Experience working with Hugging Face Transformers and its model hub.
Experience with cloud-based development and architecture.
Experience with front-end or application frameworks such as Flask.
Ability to build text-focused machine learning solutions (classification, topic modeling) using SciKit-Learn or deep-learning approaches.
Experience developing applications for semantic search.
Proficiency creating dashboards and visualizations in Tableau.
Ability to clearly communicate modeling decisions, methodologies, and results to technical and non-technical audiences.
Experience building or using verification and validation test benches.
Familiarity with Explainable AI (XAI) techniques.
Experience working with ONNX (Open Neural Network Exchange).
U.S. Citizenship Required.
Travel: Less than 10%.
Top Secret SCI + Polygraph.

Technologies

Keras, PyTorch, Tensorflow, Python, R, SQL
GitHub, Jenkins
Hugging Face Transformers, Flask
SciKit-Learn, Tableau
ONNX (Open Neural Network Exchange)
GPUs, Large Language Models (LLM), Natural Language Processing (NLP), XAI

Benefits

401K with company match
Comprehensive health and wellness packages
Internal mobility team dedicated to helping you own your career
Professional growth opportunities including paid education and certifications
Cutting-edge technology you can learn from
Paid vacation and holidays to rest and recharge

Role Details

Req#: RQ229011
Requisition Type: Regular
Public Trust: None
Location: McLean, Virginia (Onsite Workplace)
Years of Experience: 8 + years of related experience
Travel Required: Less than 10%
Citizenship: U.S. Citizenship Required

Compensation

The likely salary range for this position is $168,300 - $227,700 per year.

Identity Verification Process

On camera during virtual interviews
We reserve the right to take your picture to verify your identity and prevent fraud
Authorize the collection, processing, and use of your biometric data for identity verification and security purposes

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3d ago

Principal Machine Learning Engineer

Oracle United States

United States

3d ago

Full time Ai Ml Artificial Intelligence Data Engineering

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Job Description

Oracle is hiring a Principal Machine Learning Engineer in the United States (onsite) to implement and productionize machine learning models. In this role, you will focus on deployment readiness, monitoring in production, data quality and privacy evaluation, troubleshooting, and building internal tools and platforms that support continuous improvement.

Responsibilities

Implement machine learning models for production and ensure they are ready for deployment.
Automate ML workflows end to end, including ETL (data extraction, transformation, and loading), model deployment, and monitoring to support CI/CD for ML solutions.
Create infrastructure and frameworks to monitor model performance, including alignment with design criteria of trained models and systems.
Proactively monitor deployed model performance and troubleshoot issues independently or in collaboration with data science.
Evaluate potential data quality, security, and privacy issues and their impacts on modeling; minimize risks to data analysis and model outcomes.
Support troubleshooting and debugging for machine learning infrastructure and workflows, developing robust solutions to prevent future problems.
Collaborate with stakeholders to integrate ML models into new or existing systems, including coordination with Development Leads, Product Management, Operations, and Release Management.
Scale models, clean model code, and ensure production quality standards are met to keep models deployment-ready.
Develop novel metrics that provide analytical insights to non-technical stakeholders about how well ML models are operating.
Perform data cleaning, preprocessing, and feature identification tasks to enable model training.
Maintain an effective partnership between model development and operations to support smooth deployment and ongoing model improvement.
Address operational considerations of model deployment, such as performance, scalability, stability, and maintenance.
Develop, maintain, and refine tools, platforms, environments, and services for internal use.
Develop efficient, bug-free medium-complexity code from scratch and properly maintain and organize the existing codebase.
Apply best practices for version control, code review, and code delivery and deployment.
Build and maintain professional documentation for technical processes including experimentation, data collection and analyses, and model building.
Test and review code for bugs, and stay current on third-party ML frameworks and libraries to evaluate performance and scalability.
Manage and coordinate moderately complex tasks, monitoring timelines and deliverables to ensure on-time completion and adherence to requirements.
Delegate, monitor, and prioritize work across multiple projects, providing technical oversight and adjusting plans based on changes in resources or timelines.
Leverage understanding of business leaders, stakeholders, and customers to ensure proposed solutions meet needs.
Support inclusivity by actively seeking and listening to diverse perspectives.
Identify and address moderately complex issues by analyzing a wide range of data and information using standard practices, and escalate unresolved or critical issues with thorough assessment and possible solutions.
Review, contribute to, and document problem-solving strategies.
Pursue learning opportunities, seek feedback and training, and proactively stay abreast of latest industry trends and best practices.
Coach and mentor junior team members to foster continuous learning and knowledge sharing within and across teams.
Recommend and help implement process improvements across teams, evaluate their impact on key stakeholders, and solicit feedback on alternative approaches.
Contribute to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.

Technologies

PyTorch
TensorFlow
Keras

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3d ago

Senior ML Software Engineer

Worldpay (UK) Limited Cincinnati, OH

Cincinnati, OH

3d ago

Full time Backend Developer Senior APIs

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Job Description

Build and operate production ML services for payment optimization, with end-to-end ownership across backend, APIs, reliability, and delivery.

Responsibilities

Design, implement, and deploy software features, backend services, and APIs that operationalize ML models in production
Provide full technical ownership for existing and new production ML services in your product area, aligning engineering investments with business goals and best practices
Steward service reliability and performance using SLO-driven practices
Evolve shared platform tooling and standards to improve developer velocity, reliability, and repeatable delivery across teams
Maintain and enhance automated CI/CD pipelines, testing frameworks, and monitoring/logging
Own pre-release testing, rollouts, and release coordination for your services
Participate in on-call rotations and lead incident response, root cause analysis, and remediation for your product area
Mentor teammates and provide technical guidance to engineers and data scientists; drive continuous improvement and knowledge sharing

Requirements

5+ years as a software engineer, MLOps Engineer, or similar role, with hands-on production experience operating backend services or ML-backed APIs
Proven ability to deploy ML models as production services, including API design and integration with services
Strong Python skills and service/backend engineering experience (ideally FastAPI/OpenAPI, Flask, Go, or Java)
Production experience with containerization and orchestration (ideally Kubernetes such as EKS or OpenShift), including Docker
Experience with IaC (ideally Terraform or Terraform Cloud)
Ownership experience across CI/CD pipelines, monitoring/alerting, and on-call/incident response
Ability to monitor, troubleshoot, and optimize production ML systems, including latency, throughput, and availability
Excellent communication and cross-functional collaboration skills
Ability to work independently in ambiguous environments, resolve blockers, and deliver with urgency

Technologies

Python
FastAPI
OpenAPI
Flask
Go
Java
Kubernetes
EKS
OpenShift
Docker
IaC
Terraform
Terraform Cloud

Bonus (If You Have)

Experience with MLFlow/model versioning, SageMaker Pipelines, or Databricks
Familiarity with real-time, high-volume data products and low-latency data stores (e.g., DynamoDB)
Automated testing and validation frameworks for models and services
Experience in regulated industries (e.g., PCI, HIPAA, SOC2), large companies, or mature engineering teams
Optional familiarity with LLM tooling / RAG