Research Engineer, Evaluations - Meta Superintelligence Labs (Technical Leadership)

Meta•Menlo Park, CA

10h•$219,000 - $301,000

About The Position

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 technical role requiring 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.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
5+ 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 PyTorch
Experience identifying, designing and completing medium to large technical features independently, without guidance
Software engineering practices including version control, testing, and code review practices
Demonstrated experience of working independently and adapting to rapidly changing priorities

Nice To Haves

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

Responsibilities

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 model lines and product areas
Contribute to evaluation tooling that measures the quality and reliability of evaluation suites