---
title: 'Machine Learning Engineer at Relace'
canonical: 'https://feeny.ai/job/machine-learning-engineer-relace-san-francisco-krnc269vmpmj'
type: 'job'
last_seen: '2026-09-11'
---

# Machine Learning Engineer at Relace

- **Company:** Relace
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-10-29
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.ashbyhq.com/relace/2deb110b-b848-4316-a120-f1864d788e94

## Job description

## 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 engineering.
- Experience with CUDA, GPU kernel optimization, and performance tuning.
- Fluency in Python and at least one systems language (C++ or Rust preferred).
- Familiarity with distributed training frameworks (e.g., PyTorch, JAX, DeepSpeed, or similar).
- Experience working with large-scale training or inference infrastructure.
- Understanding of memory management, parallelization, and hardware-aware model optimization.
- 2+ years of experience working in ML infrastructure or performance-critical environments.
- Willingness to work in-person from our SF office in FiDi.

## About Relace

## Company Overview
- **One-liner**: Relace builds specialized AI models and infrastructure for coding agents, enabling fast, cheap, and reliable code retrieval and merging.
- **Entity Type**: Private (startup; Y Combinator Winter 2023)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2022
- **Founders**: Preston Zhou and Eitan Borgnia

## Core Business
- **Primary industry**: AI infrastructure for software development (coding agents, code generation)
- **Target customers**: B2B – AI codegen startups, engineering teams, and enterprises integrating autonomous coding agents into their workflows.
- **Mission**: Build the models and infrastructure to make coding agents a seamless part of all software systems – turning static SaaS pages into malleable user-driven interfaces.

## Products & Services
- **Instant Apply**: A universal code merging model that applies file edits at >10,000 tokens per second (end-to-end latency ~900ms). Reduces token usage by ~40% compared to full-file rewriting with frontier LLMs. [relace.ai](https://relace.ai/)
- **Embedding + Code Reranker**: Semantic search that retrieves relevant context from million-line codebases in ~1–2 seconds, cutting input token usage by over 50% and improving generation quality. [ycombinator.com](https://www.ycombinator.com/companies/relace)
- **Source Control for Agents**: Lightweight push/pull operations, fast branching for subagents, automatic indexing for two-stage retrieval, and rate limits designed for high throughput. [relace.ai](https://relace.ai/)
- **Self-Hosted / VPC Deployments**: On-premise or VPC-isolated deployments for teams with strict compliance or latency requirements. [relace.ai](https://relace.ai/)

## Market Standing
- **Valuation**: Not publicly disclosed (latest round: $23M raised as of Oct 2025 per YC news) [ycombinator.com](https://www.ycombinator.com/companies/relace)
- **Key Metric**: Total funding – $23M (announced October 2025)
- **Notable Investors/Partners**: Y Combinator (Winter 2023 batch), plus production partnerships with Lovable, Magic Patterns, Codebuff, Create, Tempo Labs, and 20+ other AI codegen startups. [ycombinator.com](https://www.ycombinator.com/companies/relace)
- **Growth Signals**: Models running millions of times per week in production; SOC 2 compliant; team of 7 (as of YC profile); hiring across multiple roles (ML, engineering, GTM). [builtin.com](https://builtin.com/company/relace)

## Competitive Advantages
- **Purpose-built SLMs**: Unlike general-purpose LLMs, Relace trains specialized small language models (SLMs) for retrieval and merging – achieving order-of-magnitude improvements in latency and cost without sacrificing accuracy. [relace.ai](https://relace.ai/)
- **Co-optimized infrastructure**: Models and infrastructure are designed together for coding agent workflows, enabling 10k+ tok/s merging and sub-second codebase search. [ycombinator.com](https://www.ycombinator.com/companies/relace)
- **Strong adoption**: Already integrated by leading AI codegen platforms, proving real-world performance and reliability.

## Strategic Focus
- **Expand model capabilities**: Continue training specialized SLMs for additional coding agent tasks (e.g., debugging, testing).
- **Deepen enterprise readiness**: Self-hosted/VPC deployments and SOC 2 compliance signal a push into larger, compliance-sensitive customers.
- **Build the “rails for software on-demand”**: Enable coding agents to be embedded into any software system, making interfaces malleable and user-driven.

## Why Work Here
- **Culture**: In-person team in San Francisco; described as “ex-academics, founders, and hackers” who are “stubbornly optimistic” about solving hard technical problems. [relace.ai/about-us](https://relace.ai/about-us)
- **Work policy**: In-office (San Francisco) with a hybrid workspace option for some roles. [builtin.com](https://builtin.com/company/relace)
- **Engineering focus**: High agency, early-stage environment where engineers build core AI models and infrastructure from scratch. Roles include Machine Learning Scientist, Machine Learning Engineer, Infrastructure Engineer, Product Engineer (Frontend), and GTM Engineer. [jobs.ashbyhq.com/relace](https://jobs.ashbyhq.com/relace)
- **Impact**: Work on models used millions of times per week by leading AI codegen startups – direct influence on the future of software development.

## Sources
1. [relace.ai](https://relace.ai/)
2. [relace.ai/about-us](https://relace.ai/about-us)
3. [ycombinator.com/companies/relace](https://www.ycombinator.com/companies/relace)
4. [builtin.com/company/relace](https://builtin.com/company/relace)
5. [jobs.ashbyhq.com/relace](https://jobs.ashbyhq.com/relace)

## Other roles at Relace

- [Product Engineer (Frontend)](https://feeny.ai/job/product-engineer-frontend-relace-san-francisco-s47yehc0311k) — San Francisco, CA
- [Machine Learning Scientist](https://feeny.ai/job/machine-learning-scientist-relace-san-francisco-x6cd3a3vkg65) — San Francisco, CA
- [Developer Relations Advocate](https://feeny.ai/job/developer-relations-advocate-relace-san-francisco-vxvkyvpq0gwt) — San Francisco, CA
- [Growth & Marketing Engineer](https://feeny.ai/job/growth-marketing-engineer-relace-san-francisco-pjhd6kdw85td) — San Francisco, CA
- [Infrastructure Engineer](https://feeny.ai/job/infrastructure-engineer-relace-san-francisco-zf8xjm22pchz) — San Francisco, CA
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