Research Engineer / Scientist

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 leadership 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.