Machine Learning Scientist
Relace · San Francisco
No salary listed. Estimated from 344 salary-disclosed Research roles on this board: $225K–$340K (interquartile range; estimate, not the employer's figure).
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Relace is building the models and infrastructure that code agents reach for. We power the fastest model on OpenRouter (10,000 tok/s) and deliver optimized small language models designed for retrieval, application, and core code generation functions.
Our technology supports some of the world’s fastest-moving companies — including Lovable, Figma, and Vercel — as they deploy and scale code generation to hundreds of millions of users. We recently raised our Series A from a16z, and we’re growing quickly.
Our team is made up of mathematicians, physicists, and computer scientists who are deeply passionate about their craft. If you thrive on ambitious technical problems, care about elegant systems design, and want to build the foundation of how code gets written at scale, this is the place for you.
The RoleWe’re looking for a Machine Learning Scientist to push the limits of small, high-performance language models. This is a deeply technical role focused on advancing the capabilities of our models for retrieval, application, and code generation.
The ideal candidate has a strong background in ML research and engineering, is comfortable working with both theory and production systems, and thrives in an environment where ideas turn into deployed infrastructure fast. This person should be excited to work on training methodology, optimization, evaluation, and model architecture at scale — and collaborate directly with infrastructure and product teams to get breakthroughs into production quickly.
This role is best suited for someone who loves both mathematical elegance and real-world impact.
RequirementsStrong background in machine learning, deep learning, or related fields.
2+ years of experience working on ML research or production systems.
Fluency in Python and frameworks like PyTorch or JAX.
Experience with training and optimizing large or efficient models.
Strong understanding of applied optimization, distributed training, or model evaluation.
Familiarity with code models, retrieval systems, or language modeling a plus.
Advanced degree (MS or PhD) in a quantitative field, or equivalent industry experience.
Willingness to work in-person from our SF office in FiDi.
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