Machine Learning Model Training

Machine Learning Model Training Developer Jobs

5 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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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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4d ago

Founding Machine Learning Engineer

Arcade San Francisco, CA

San Francisco, CA

4d ago

$230k+/yr Full time Ai Ml Artificial Intelligence Data Pipeline

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Job Description

Arcade is hiring a Founding Principal Machine Learning Engineer to build and productionize small, fast, on-prem, agentic tool recommendation and runtime ML models.

Responsibilities

Run Arcade’s ML pipeline end to end, from data and training through evaluation and release
Make model shipping routine by increasing reliability across training, eval, and deployment
Fine-tune and train models for tool selection, routing, retrieval, and agent memory
Use the right techniques for the job, including distillation, embeddings, rerankers, and more
Expand into new territory starting with search and recommendation across tools and agent context
Build and operate an evaluation system:
- Offline evals against real agent traces
- Online measurement in production
- Head-to-head comparisons with Claude, GPT, and Gemini
Close the loop from production agent traces and tool-call data to improved models, within enterprise data boundaries
Quantize, optimize, and package models for customer VPCs and air-gapped environments; coordinate with the Runtime team on serving
Choose the ML stack and write the strategy; help determine who to hire next in ML
Apply expertise in modern agent systems (harnesses, memory, skills) to identify where models meaningfully improve agent capabilities and to call early if an approach won’t work
Drive iteration speed: projects that take a week should take a day
Own the model pipeline lifecycle from data ingestion to model publishing, including models across:
- embedding
- reranking
- classification
- recognition
- other agent-related tasks
Stand by deployed models backed by able data to cover outcomes; be willing to admit faults and review issues (including row-by-row spreadsheet work)
Ensure everything runs in customer environments: their cloud, their hardware, and sometimes air-gapped networks
Provide stable-but-young pipeline improvements based on insight and experience
Lead a group making agent capabilities, not only API calls, with intelligent and efficient abilities that outpace competitors
Own the models on the runtime path of every agent call served
Productionize the first agent recommendation model and set the patterns for future work
Own build-buy decisions for the stack with a healthy budget to spend
Report directly to the Head of Engineering

Requirements

7+ years of software engineering experience; 4+ years training and shipping production ML systems
Experience training or fine-tuning models that reached production and moved metrics customers cared about
Expertise in production agent systems: harnesses, memory, skills, tool use, and sub-agents
Know how and when fine-tuning works and how to apply it effectively
Defensible evals with statistics fluency to determine whether deltas are real or noise
Deployed models under real constraints: tight latency budgets, limited GPUs, or third-party infrastructure (vLLM, ONNX, TensorRT, llama.cpp, or equivalent)
Strong Python for training and ML work; TypeScript or Go for production services that serve models
Ability to pick the stack, document decisions, and defend them over time
Preference for shipping a working v0.5 over a polished v2.0 on a later quarter timeline
Comfort with ambiguity in an early team environment where the charter expands and decisions are made with incomplete data
Strong drive to ship

Technologies

Python, TypeScript, Go
vLLM, ONNX, TensorRT, llama.cpp
Claude, GPT, Gemini
MCP

Compensation and Benefits

Competitive salary, equity, and benefits
$230,000 per year starting base salary (range aligned; determined by background, experience, and performance)
In person at the San Francisco office

Location

San Francisco, CA (onsite)

Traction

Real deployments with Fortune-100 customers including Morgan Stanley and Open Table

Bonus Points

Shipped ML into enterprise on-prem or regulated environments (financial services, healthcare, government)
Tool-use benchmark or eval experience: BFCL, τ-bench, ToolBench, MCP evals, or equivalent
Familiarity with the MCP ecosystem (extra bonus for filing an issue against the spec)
Built training-data pipelines from production traces with privacy controls
First ML hire experience at an early-stage company, with willingness to do some things differently
Open-source contributions or published work that held up with other engineers
Experience at an early-stage startup

Founder-Market Fit

CEO previously founded Stormpath (acquired by Okta), creating the first Authentication API for developers
CTO led the vector database team at Redis, shipped 100+ LLM applications, and contributes to LangChain and LlamaIndex

Dream Team

Authentication, integrations, distributed systems, and AI experts from Okta, Redis, Microsoft, Splunk, Ngrok, Google, Airbyte, Disney, and HPE
Team members have built and founded multiple successful developer platforms

Timing and Market

Enterprise demand: almost none have agents in production yet
Building critical infrastructure for a major technological shift

Backed By the Best

Series A led by SYN Ventures with strategic investment from Morgan Stanley and Wipro
Earlier investors include Databricks, Clickhouse, MongoDB, Perplexity, Cohere, ScaleAI, Confluent, Elastic, and Firebase

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4d ago

Senior Machine Learning Engineer, Fraud Risk Modeling

GEICO Palo Alto, CA

Palo Alto, CA

4d ago

$115k - $230k/yr Full time Artificial Intelligence Automation Big Data

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Job Description

GEICO is seeking a Senior Machine Learning Engineer, Fraud Risk Modeling in Palo Alto, CA (onsite). In this role, you will lead the design, implementation, and deployment of production-grade machine learning models, with opportunities to build scalable ML infrastructure and own end-to-end model lifecycle management. GEICO provides competitive pay and a supportive environment focused on growth, mentorship, and certification assistance.

What you’ll do

Lead model design and implementation, partnering with Product, Business Units, and Engineering teams on fraud risk modeling solutions.
Build scalable ML infrastructure for model training, automated hyperparameter tuning, and deployment pipelines that stay reliable and performant at scale.
Develop production-grade ML services and APIs, writing modular and reusable code that turns models into deployable components for future projects.
Optimize model performance by debugging and troubleshooting issues, tracking key metrics, and improving reliability, speed, and efficiency in production.
Own the full ML lifecycle, including monitoring, retraining, and version management to keep models aligned with business needs over time.
Provide leadership and mentorship to junior machine learning engineers, promoting best practices across software engineering, model development, and deployment.
Collaborate across teams such as data engineering, software development, and product management to integrate models and support smooth production operations.
Stay current with industry trends by exploring new techniques and system engineering tools to advance team practices in machine learning and systems architecture.

What you bring

B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.
6+ years of hands-on experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments.
6+ years using open-source and/or cloud-agnostic components such as Snowflake, Kafka, PostgreSQL, MongoDB, Cassandra, Spark, Ray, Airflow, and Temporal.
6+ years professional software development experience with at least two general-purpose languages, such as Java, C++, Python, or C#.
6+ years with machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
At least 4 years with cloud platforms (AWS, Azure, GCP) and containerization (Docker), plus orchestration with Kubernetes.
Proven experience deploying ML models in production with scalability, reliability, and high availability in mind.
Strong engineering fundamentals: object-oriented design, design patterns, clean maintainable code, Git, and familiarity with Agile.
Distributed systems expertise for scaling ML models in production, including distributed data processing and microservices architectures.
Experience with MLOps such as CI/CD, automated testing, and deployment pipelines for ML models.
System architecture and performance optimization, including designing fault-tolerant systems for large-scale data and high-volume requests.
Experience deploying on AWS/Azure/GCP and familiarity with AWS SageMaker, GCP AI Platform, and Azure Machine Learning.
Monitoring and logging experience to track performance and ensure efficient resource utilization.

Helpful background (preferred)

Experience designing and building high-performance distributed systems for large-scale data ingestion and processing for ML workloads.
Experience with real-time inference pipelines and low-latency model serving.
Familiarity with serverless or managed services for ML model deployment.
Advanced degree (M.Sc., Ph.D.) in a related field.
Experience with GPU/TPU optimization for accelerated training and inference.

Tools you may work with

Java, C++, Python, C#, TensorFlow, PyTorch, Scikit-learn, AWS, Azure, GCP, Docker, Kubernetes, snowflake, Kafka, PostgreSQL, MongoDB, Cassandra, Spark, Ray, Airflow, Temporal, Git, AWS SageMaker, GCP AI Platform, Azure Machine Learning

Compensation and GEICO pledge

Annual salary: $115,000.00 - $230,000.00

Great Company: Protecting customers through life’s twists and turns with innovation and integrity.
Great Careers: Personalized development programs, mentorship, and certification assistance.
Great Culture: Inclusive and collaborative culture rooted in shared success.
Great Rewards: Competitive pay, benefits, and flexibility to support your well-being and future.

Benefits

Competitive pay, benefits, and flexibility to support your well-being and future.
Personalized development programs, mentorship, and certification assistance.

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4d ago

Senior Machine Learning Engineer

Microsoft Cambridge, MA

Cambridge, MA

4d ago

$120k - $261k/yr Full time Senior Ai Ml Artificial Intelligence

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Job Description

Microsoft is hiring a Senior Machine Learning Engineer for an onsite role in Cambridge, MA. In this position, you will help design, train, and optimize generative models that support multimodal scientific discovery, partnering closely with researchers and domain experts to move new ideas from research into practical impact.

This work involves building and scaling large training workflows, improving efficiency across training runs, and providing technical direction across the team as model capability advances throughout the training lifecycle.

Role overview

You will lead efforts that span method development, recipe refinement, scaling and throughput optimization, and model-harness co-design. The approach is iterative, with continued investment across the training lifecycle to advance generative model capabilities for multimodal scientific use cases.

Microsoft Research New England brings together full-time researchers, postdocs, data scientists, engineers, and program managers spanning machine learning, statistics, healthcare, computational biology, theoretical computer science, algorithmic game theory, economics, and more.

Responsibilities

Lead the training of large language and multimodal models, including developing, managing, and debugging training workflows
Scale and optimize training runs by identifying efficiency opportunities and implementing improvements
Mentor and provide technical leadership across the team and organization in relevant areas of expertise
Explore and advise on strategic direction and technical approaches for projects across the team
Collaborate with researchers, domain experts, and cross-disciplinary teams to ensure techniques translate the latest research into impactful solutions

Requirements

Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience in ML-related work, or a Doctorate in Computer Science or related technical field
Hands-on technical experience in distributed training of large language and/or multimodal models (pretraining, midtraining, or post-training), optimizing training workflows, or model distillation
Deep experience with PyTorch or Jax
Experience designing, optimizing, and monitoring generative modeling training workflows
Familiarity with profiling training and/or inference code, identifying performance bottlenecks, and mitigating them
Comfort with one or more areas including refining data mixes, midtraining, post-training, data/model parallelism techniques, optimizing training runs, or model distillation
Demonstrated communication skills, including the ability to present complex technical concepts to a broad audience

Technologies

PyTorch
Jax

Compensation: USD 119,800 - 261,000 per yearly.

Experience level: Minimum 4 years.

Education: Bachelor's Degree in Computer Science or related technical field.

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Machine Learning Model Training Developer Jobs

Senior Machine Learning Engineer - Automation for Translation and Multilingual Intelligence

Machine Learning Engineer - E-Commerce Recommendation Live

Founding Machine Learning Engineer

Senior Machine Learning Engineer, Fraud Risk Modeling

Senior Machine Learning Engineer

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