AIML - Sr Machine Learning Engineer, Data and ML Innovation
Oct 8, 2026 Cupertino, CA
About us
Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.
- Size: 10,000+ employees
- Industry: Technology: Manufacturing
Why you should apply for a job to Apple:
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3.1/5 in supportive management
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65% would recommend this company to other women
66% say the CEO supports gender diversity
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Do you want to play a part in the revolution in Foundation Models? Contribute to model hillclimbing for Apple Intelligence features that leverage Apple Foundation Models, and work with the people who built the intelligent products that helps millions of people get things done - just by asking or typing? The vision for AIML FM Data organization is to improve Foundation Models by leveraging data and cutting-edge LLM techniques. As a Sr ML Engineering on the team, you will drive ML innovations, identify key opportunity areas and experiment with various techniques to improve model training and evaluation efficiency and performance.
Description
As a Senior Machine Learning Engineer, you will join end-to-end development of large language models and agentic systems, from training pipelines to evaluation frameworks and production deployment. You will work at the intersection of modeling, infrastructure, and product, helping push model quality through systematic experimentation and iteration. You'll collaborate closely with research, infrastructure, and product teams to design robust training pipelines, build agent environments, and ship high-impact AI capabilities into real-world applications. This role blends deep modeling expertise with strong engineering fundamentals and offers the opportunity to shape both the technical direction and the ML platform powering Apple products.","responsibilities":"Model Training & Optimization
Design and implement large-scale LLM pretraining and post-training pipelines, including supervised fine-tuning, preference optimization, and continual learning.
Drive model hillclimbing through disciplined experimentation: dataset curation, hyperparameter tuning, and ablation studies.
Work on scalable training workflows using distributed frameworks.
Evaluation, Reward, and Data Systems
Develop evaluation frameworks for both offline benchmarks and online metrics, covering reasoning, tool use, and task success.
Design and maintain verifiers / rubric-based reward systems for agentic tasks and model alignment.
Build data pipelines for data generation, filtering, labeling, and replay buffers.
Agent & Environment Infrastructure
Build and maintain agent training environments, including tool APIs, simulators, and sandboxed runtimes.
Implement environment abstractions to support reinforcement learning and agent evaluation at scale.
Collaborate on large scale RL-infra: RL-trainer, rollout system, and containerized environments.
Preferred Qualifications
Direct experience with agentic systems, including tool use, environment design, or reinforcement learning.
Experience with building or operating training environments or simulators (gym-style, tool-based, or sandboxed environments).
Experience with model hillclimbing workflows: systematic experimentation, ablations, dataset iteration, and continuous quality improvement.
Ability to work across research and engineering boundaries, turning ideas into scalable systems.
Have demonstrated creative and critical thinking with an innate drive to improve how things work. Have a high tolerance for ambiguity.
Minimum Qualifications
5+ years of hands on ML engineering experiences, with at least 1+ years working directly on large language models or generative AI.
Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or a related technical field - or equivalent practical experience.
Hands-on experience with LLM training workflows, including one or more of: Pretraining or continued pretraining, Supervised fine-tuning (SFT), Preference optimization (e.g., RLHF, DPO, PPO).
Strong software engineering fundamentals: debugging, testing, code reviews, and production reliability.
Demonstrated publication records in relevant conferences (e.g., NeurIPS, ICML, ICLR, etc.).
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Description
As a Senior Machine Learning Engineer, you will join end-to-end development of large language models and agentic systems, from training pipelines to evaluation frameworks and production deployment. You will work at the intersection of modeling, infrastructure, and product, helping push model quality through systematic experimentation and iteration. You'll collaborate closely with research, infrastructure, and product teams to design robust training pipelines, build agent environments, and ship high-impact AI capabilities into real-world applications. This role blends deep modeling expertise with strong engineering fundamentals and offers the opportunity to shape both the technical direction and the ML platform powering Apple products.","responsibilities":"Model Training & Optimization
Design and implement large-scale LLM pretraining and post-training pipelines, including supervised fine-tuning, preference optimization, and continual learning.
Drive model hillclimbing through disciplined experimentation: dataset curation, hyperparameter tuning, and ablation studies.
Work on scalable training workflows using distributed frameworks.
Evaluation, Reward, and Data Systems
Develop evaluation frameworks for both offline benchmarks and online metrics, covering reasoning, tool use, and task success.
Design and maintain verifiers / rubric-based reward systems for agentic tasks and model alignment.
Build data pipelines for data generation, filtering, labeling, and replay buffers.
Agent & Environment Infrastructure
Build and maintain agent training environments, including tool APIs, simulators, and sandboxed runtimes.
Implement environment abstractions to support reinforcement learning and agent evaluation at scale.
Collaborate on large scale RL-infra: RL-trainer, rollout system, and containerized environments.
Preferred Qualifications
Direct experience with agentic systems, including tool use, environment design, or reinforcement learning.
Experience with building or operating training environments or simulators (gym-style, tool-based, or sandboxed environments).
Experience with model hillclimbing workflows: systematic experimentation, ablations, dataset iteration, and continuous quality improvement.
Ability to work across research and engineering boundaries, turning ideas into scalable systems.
Have demonstrated creative and critical thinking with an innate drive to improve how things work. Have a high tolerance for ambiguity.
Minimum Qualifications
5+ years of hands on ML engineering experiences, with at least 1+ years working directly on large language models or generative AI.
Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or a related technical field - or equivalent practical experience.
Hands-on experience with LLM training workflows, including one or more of: Pretraining or continued pretraining, Supervised fine-tuning (SFT), Preference optimization (e.g., RLHF, DPO, PPO).
Strong software engineering fundamentals: debugging, testing, code reviews, and production reliability.
Demonstrated publication records in relevant conferences (e.g., NeurIPS, ICML, ICLR, etc.).
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Additional Information
- Salary: $150,400 – $277,600
- Posted: Oct 8, 2026