Research Engineer / Scientist

Apple is revolutionizing artificial intelligence by developing sophisticated foundation models that power intelligent features across our product ecosystem. Were seeking skilled Machine Learning Engineers to transform cutting-edge research into scalable, production-ready AI solutions. We are looking for engineers who are passionate about building systems that push the frontier of deep learning in terms of scaling, efficiency, and flexibility and delight millions of users in Apple products.

We build the foundation models behind intelligent experiences across Apple products, from high-performance server models to highly efficient on-device models, all designed for Apple silicon and for private, personal use. How good these models get depends on the data they learn from and on how well we measure them.

We are looking for a technical leader to drive training data and model evaluation across the foundation model training lifecycle, from pre-training and mid-training through post-training. You will influence our strategy for data development: data at scale, at high quality, compliant with privacy and policy requirements, and targeted through curation and synthesis based on product feedback. You will also drive evaluation that steers model training and predicts real user experience. This position combines machine learning research, model evaluation, and distributed data systems. Success is measured by the model improvements our data produces, and by evaluations that are reliable, predict real user experience, and guide training decisions.

You will work through trade-offs when data, evaluation, and training priorities compete, and bring data and evaluation insights into model decisions: which capabilities to prioritize, which data and mixture changes to adopt, and whether a model is ready for release. You will partner with model training leads, product teams, and security, legal, and compliance teams.

You are a recognized technical expert who leads across teams through technical depth and influence. Depending on your background, the role may include managing a team.

The responsibilities below cover the full scope of the area. Your focus will be the subset that best matches your background, and it can grow over time.

• Production model strategy
• Closing the loop between evaluation, data, and training
• Evaluation
• Data systems and curation
• Technical and team leadershipShape strategy for training data and evaluation across pre-training, mid-training, and post-training, aligned with the production model roadmap. Help decide where to invest across data acquisition, curation, synthetic data, and evaluation coverage, including what to build, buy, or source from vendors. Set standards that other teams adopt for benchmark governance, release gating, contamination, and data documentation. Turn evaluation failures into data decisions: decide which capability gaps to close through acquisition, curation, or synthetic generation, and verify the result. Lead data ablations and scaling-law studies, and define how data mixture decisions are made and validated. Lead decontamination across pre-training, mid-training, and post-training data, including synthetic and vendor data. Define the standard, measure residual contamination, and protect held-out evaluation sets from overuse. Define the evaluation portfolio for reasoning, coding, knowledge, and multi-turn agentic workflows, and decide where to build new benchmarks and where to adopt existing ones. Lead model-based judging, including how judges are validated, versioned, and checked for bias. Build product-aligned evaluation that predicts user-perceived quality on Apple devices, and partner with annotation teams and vendors to correlate automated metrics with human judgment. Direct the architecture of high-throughput systems that acquire, process, and curate petabytes of text, code, and multimodal data (image, audio, video) across cloud and high-performance compute. Set the quality bar for algorithmic data curation, including semantic deduplication, heuristic and model-based quality filtering, and synthetic data generation. Drive data provenance, licensing, privacy, and takedown practices for training data with security, legal, and compliance teams. Raise the technical bar across data and evaluation through design reviews, mentoring, and hiring, and drive adoption of shared systems and tooling across teams. Where the role includes a team, manage and grow it. Communicate findings, risks, and recommendations clearly to senior leadership and cross-functional partners.PhD and 5+ years or MS and 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 the 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: setting direction, mentoring senior engineers or researchers, and driving adoption of systems, benchmarks, or standards beyond your own team. Ability to influence senior leaders and cross-functional partners, and to turn complex results into clear decisions. Hands-on proficiency in Python and an ML framework such as PyTorch or JAX.PhD in a related field. Training data: experience across pre-training, mid-training, and post-training data, including data ablations and scaling-law studies, and with non-text modalities (images, audio, video) and source code at scale. Evaluation: experience designing standardized LLM benchmarks, LLM-as-a-judge frameworks, contamination detection and decontamination, or human evaluation programs with annotation teams and vendors. Systems: experience architecting distributed data pipelines (for example Apache Beam, Spark) on major cloud platforms, strong systems programming skills, and building evaluation or data platforms adopted across multiple teams. Governance: experience with data provenance, licensing, and privacy-preserving practices for training data. Experience managing a team of engineers or researchers.