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AIML - Sr Machine Learning Engineer, Data and ML Innovation

Apple· Machine Learning and AI
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Cupertino, CA$150.4k - $277.6kFull-timePosted 6mo ago

About the role

Senior ML Engineer on the AIML FM Data team focused on improving Foundation Models through data and ML techniques. End-to-end involvement from training pipelines to evaluation frameworks and production deployment, collaborating with research, infrastructure, and product teams to ship high-impact AI capabilities.

Responsibilities

  • 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.
  • 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.
  • 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.

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.).
  • 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.

Skills

Required:Large language models (LLMs)Distributed trainingData engineering for MLAgentic systems / RL environmentsEvaluation frameworksSoftware engineering fundamentalsResearch collaboration
Nice to have:RLHF / Reward modeling

Benefits

  • Comprehensive medical and dental coverage
  • retirement benefits
  • range of discounted products and free services
  • reimbursement for certain educational expenses — including tuition
  • discretionary bonuses or commission payments as well as relocation eligibility
  • employee stock programs and stock purchase plan
Last updated Jul 31, 2026. Interested? Apply on the company site.

About Apple

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