Machine Learning Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Onsite From the posting source checked Jun 20, 2026

In-office days

5 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Series A From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Engineer San Francisco | OnSite ABOUT US 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 ROLE We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable through low-level optimizations and smart systems design. The ideal candidate is excited by CUDA kernels, memory layouts, GPU scheduling, and squeezing performance out of complex training and inference workloads. They should be just as comfortable optimizing compute and networking paths as they are working alongside research teams to productionize new architectures. This is a role for someone who enjoys deep performance tuning, understands the realities of running large-scale ML systems, and thrives in fast-moving, high-leverage environments. REQUIREMENTS - Strong background in systems-level ML

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