Research Engineer / Scientist

Apple • Cupertino, CA

2d

About The Position

Apple is seeking a technical leader to drive training data and model evaluation across the foundation-model training lifecycle, including pre-training, mid-training, and post-training. The role focuses on developing high-quality, large-scale training data that meets privacy and policy requirements, and on evaluation methods that guide training and predict real user experience. The work supports foundation models across Apple products, including high-performance server models and efficient on-device models designed for Apple silicon and private, personal use.

The position combines machine learning research, model evaluation, and distributed data systems. The successful candidate will use data and evaluation insights to inform model capabilities, data and mixture changes, and release readiness. They will work through trade-offs among data, evaluation, and training priorities and partner with model training leads, product teams, and security, legal, and compliance teams. The role requires recognized technical expertise and cross-team influence; depending on the candidate’s background, it may include managing a team. The areas of responsibility span production model strategy, connecting evaluation, data, and training, evaluation, data systems and curation, and technical and team leadership. The role’s focus may be tailored to the candidate’s background and may grow over time.

Requirements

• PhD and at least 5 years, or MS and at least 8 years, of experience in machine learning or data infrastructure, including leading technical work at scale.
• Deep machine learning expertise in LLMs or multimodal foundation models, with depth in either evaluation methodology or large-scale training data systems and curation, and working fluency in the other.
• Strong fundamentals in statistics and experimental design, and credibility to review designs and experiments in detail.
• A track record of training-data or evaluation work on large models that measurably improved model quality or changed how models are built.
• Technical leadership across teams, including setting direction, mentoring senior engineers or researchers, and driving adoption of systems, benchmarks, or standards beyond one’s own team.
• Ability to influence senior leaders and cross-functional partners and turn complex results into clear decisions.
• Hands-on proficiency in Python and an ML framework such as PyTorch or JAX.

Nice To Haves

• PhD in a related field.
• Experience with training data across pre-training, mid-training, and post-training, including data ablations and scaling-law studies.
• Experience with non-text modalities such as images, audio, and video, and with source code at scale.
• Experience designing standardized LLM benchmarks, LLM-as-a-judge frameworks, contamination detection and decontamination, or human evaluation programs with annotation teams and vendors.
• Experience architecting distributed data pipelines, such as Apache Beam or Spark, on major cloud platforms.
• Strong systems programming skills.
• Experience building evaluation or data platforms adopted across multiple teams.
• Experience with data provenance, licensing, and privacy-preserving practices for training data.
• Experience managing a team of engineers or researchers.

Responsibilities

• Drive training data and model evaluation across the foundation-model training lifecycle, from pre-training and mid-training through post-training.
• Influence data-development strategy, including data at scale and high quality, privacy- and policy-compliant data, and curation and synthesis informed by product feedback.
• Drive evaluation that steers model training and predicts real user experience.
• Use data and evaluation insights to inform capability priorities, data and mixture changes, and decisions about model release readiness.
• Balance trade-offs among data, evaluation, and training priorities.
• Partner with model training leads, product teams, and security, legal, and compliance teams.
• Lead technical work across teams through technical depth and influence.
• Depending on background, potentially manage a team.
• Contribute across production model strategy, evaluation, data systems and curation, and technical and team leadership, with focus tailored to the candidate’s background.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree