Research Engineer - MSL FAIR Foundations

Role

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 practical 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.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 4+ years of experience in machine learning engineering, machine learning research, or a related technical role
• Proficiency in Python and experience with ML frameworks such as Py Torch
• Experience identifying, designing and completing medium to large technical features independently, without guidance
• Demonstrated experience in software engineering practices including version control, testing, and code review practices
• Ability to work independently and adapt to rapidly changing priorities
• Publications at peer-reviewed venues (NeurIPS, ICML, ICLR, ACL, EMNLP, or similar) related to language model evaluation, benchmarking, or deep learning
• Hands-on experience with language model post-training and deep learning systems, or building reinforcement learning environments
• Experience implementing or developing evaluation benchmarks for large language models and multimodal models (e.g., vision-language, audio, video)
• Experience working with large-scale distributed systems and data pipelines
• Familiarity with language model evaluation frameworks and metrics
• Track record of open-source contributions to ML evaluation tools or benchmarks