Simplify Logo
Relace

Relace

Managed AI model fine-tuning and deployment

Machine Learning Scientist

Full-Time
No salary listed
Junior
Master's, PhD
San Francisco, CA, USA
In Person

Work in person at the San Francisco office in the Financial District.

About the job

Requirements
  • Candidates must have a strong background in machine learning, deep learning, or related fields.
  • Candidates must have at least 2 years of experience working on machine learning research or production systems.
  • Candidates must be fluent in Python and frameworks such as PyTorch or JAX.
  • Candidates must have experience training and optimizing large or efficient models.
  • Candidates must have a strong understanding of applied optimization, distributed training, or model evaluation.
  • Candidates must have an advanced degree, specifically a Master of Science or PhD, in a quantitative field, or equivalent industry experience.
  • Candidates must be willing to work in person from the San Francisco office in the Financial District.
Responsibilities
  • Advance the capabilities of small, high-performance language models for retrieval, application, and code generation.
  • Work on training methodology, optimization, evaluation, and model architecture at scale.
  • Collaborate directly with infrastructure and product teams to move breakthroughs into production quickly.
Desired Qualifications
  • Familiarity with code models, retrieval systems, or language modeling is preferred.

About the company

Relace.ai helps enterprises build and deploy AI models efficiently by capturing datasets at the application level, fine-tuning models, and running them with an optimized inference engine. It provides custom deployments on dedicated GPUs or autoscaling GPU clusters, with options to use out-of-the-box training hyperparameters or work with ML engineers for task-specific tuning. The platform combines end-to-end model training and deployment with hands-on engineering support and flexible infrastructure, emphasizing task-specific optimization and data privacy. Its goal is to enable enterprises to add capable AI systems quickly and reliably through a streamlined process for data capture, model tuning, and scalable deployment while maintaining security.

Company Size

1-10

Company Stage

N/A

Total Funding

N/A

Headquarters

San Francisco, California

Founded

2022

Get referred to Relace

See people who can refer or advise you

Simplify Jobs

Simplify's Take

What believers are saying

  • Relace closed a $23M Series A on 2025-10-08, funding rapid product expansion.
  • Public beta Repos and templates lower adoption friction for teams onboarding agents.
  • Hiring six roles in San Francisco signals active buildout across infra, ML, and growth.

What critics are saying

  • GitHub Copilot added agentic coding, issue-to-PR workflows, and JetBrains support in 2026.
  • Cursor and Windsurf already own developer mindshare for autonomous editing and context retrieval.
  • If Relace fails to win enterprise repositories quickly, Copilot and GitHub absorb its category.

What makes Relace unique

  • Relace Repos reached public beta in November 2025, tightly coupling storage, indexing, and agents.
  • Its models merge AI code edits without rewriting surrounding code, reducing patch churn.
  • a16z, Matrix, and Y Combinator back Relace, validating a specialized coding-agent infrastructure wedge.

Help us improve and share your feedback! Did you find this helpful?

Company News

TechStartups
Oct 8th, 2025
Relace raises $23M in funding led by Andreessen Horowitz to build tools for AI coding agents

Relace, a young AI startup founded just a few months ago, has raised $23 million in Series A funding led by Andreessen Horowitz to build specialized infrastructure for AI coding agents, the company announced Wednesday.

The Information
Oct 8th, 2025
Relace Secures $23M for AI Coding Tools

Andreessen Horowitz led a $23 million funding round for Relace, a startup developing tools for AI coding agents. Relace's tools include AI models that merge AI-generated code edits into main codebases without rewriting surrounding code and identify useful code files for AI agents. Matrix Partners and Y Combinator also participated in the funding.