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Research Engineer - MSL FAIR Foundations

Menlo Park, CA, USScore undefined/1001w ago
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Description
Meta is seeking Research Engineers to join the Evaluations team within Meta Superintelligence Labs. Evaluations are the core of AI progress at MSL, determining what capabilities get built, which features get prioritized, and how fast our models improve. As a Research Engineer on this team, you will curate and build the benchmarks for our most advanced AI models, across text, vision, audio, and beyond. You'll work alongside world-class researchers and engineers to collect, develop, and deploy novel benchmarks and reinforcement learning environments. This is a highly technical role requiring solid research engineering skills and the ability to work independently on a variety of open-ended machine learning challenges with high reliability. The evaluations you build will directly impact the research direction and major model lines within MSL, making engineering reliability, rigor, and scalability paramount. You will excel by maintaining high velocity while adapting to rapidly shifting priorities as we advance the technical research frontier. You'll need to be flexible and adaptive, tackling a wide variety of problems in the evaluations space, from implementing existing benchmarks to developing novel benchmarks and environments to implementing evaluation tooling at scale. If you are passionate about defining the capabilities that drive AI progress and thrive in fast-paced, high-impact research environments, we encourage you to apply for this exciting opportunity at the core of MSL. Curate and integrate publicly available and internal benchmarks to direct the capabilities of frontier model development Develop and implement evaluation environments, including environments for novel model capabilities and modalities Collaborate with external data vendors to source and prepare high-quality evaluation datasets Execute on the technical vision of research scientists designing new benchmarks and evaluations Build robust, reusable evaluation pipelines that scale across multiple mo
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