2 days ago
Responsibilities
- Drive training-data strategy across pre-training, mid-training, and post-training, including data quality, scale, privacy, curation, and synthesis.
- Design and lead model evaluation that reliably predicts user experience and guides training and release decisions.
- Close the loop between evaluation, data, and model training priorities.
- Develop production model strategy and determine capability, data-mixture, and model-readiness decisions.
- Architect distributed data pipelines and evaluation or data platforms adopted across teams.
- Lead technical direction, mentor senior engineers or researchers, and drive adoption of systems, benchmarks, and standards.
- Partner with model-training leads, product teams, and security, legal, and compliance teams.
- Potentially manage a team of engineers or researchers.
Requirements
- PhD and 5+ years or MS and 8+ years of experience in machine learning or data infrastructure, including leading technical work at scale.
- Deep expertise in LLMs or multimodal foundation models, with depth in evaluation methodology or large-scale training-data systems and working fluency in the other area.
- Strong fundamentals in statistics and experimental design, with the ability to review designs and experiments in detail.
- Demonstrated training-data or evaluation work on large models that measurably improved model quality or changed model-development practices.
- Experience setting direction across teams, mentoring senior technical staff, and driving adoption beyond an immediate team.
- Ability to influence senior leaders and cross-functional partners and communicate complex results as clear decisions.
- Hands-on proficiency in Python and an ML framework such as PyTorch or JAX.
- Preferred experience with pre-training, mid-training, and post-training data; data ablations; scaling-law studies; non-text modalities; and source code at scale.
- Preferred experience designing LLM benchmarks, LLM-as-a-judge frameworks, contamination detection and decontamination methods, or human evaluation programs.
- Preferred experience architecting distributed data pipelines using technologies such as Apache Beam or Spark on major cloud platforms.
- Preferred experience with systems programming, data provenance, licensing, privacy-preserving training-data practices, and managing engineering or research teams.
- A PhD in a related field is preferred.
Tech Stack
Categories
AI ResearchML Engineering
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.