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Applied Compute

Custom AI models and enterprise agents

Applied Research Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Meaningful AI research experience from industry or academic work.
  • Comfort working in unfamiliar codebases and training models on real-world, messy data.
  • Ability to work directly with customers by listening, empathizing, and understanding how their organizations operate.
  • Desire to own problems end-to-end and deliver outcomes.

Responsibilities

  • Train and post-train enterprise models, from environment design through deployment.
  • Own customer engagements end-to-end, working directly with technical and business stakeholders.
  • Navigate new customer architectures, data systems, and requirements quickly.
  • Surface customer learnings and contribute them back to the platform.
  • Collaborate across research, product, and infrastructure to define architectures, evaluation frameworks, and best practices.

Desired Qualifications

  • Experience training or fine-tuning language models in production settings.
  • Background building reinforcement learning environments, evaluation frameworks, or agent systems.
  • Previous experience as a founder or early engineer at a zero-to-one company.
  • Technical creativity demonstrated through side projects, open-source contributions, or published work.
  • Experience at companies with forward-deployed or customer-facing engineering cultures.

About the Company

Applied Compute

Custom AI models and enterprise agents

Applied Compute creates customized AI capabilities for enterprises by building proprietary AI models and in-house AI agents, a concept they call Specific Intelligence. Its products train on a company’s own data and are fine-tuned for specific workflows, delivered via a vertically integrated platform that includes training infrastructure, an agent platform, and developer tools on a GPU cluster, with engineers embedded inside customer teams to accelerate deployment. The company differentiates itself by focusing on proprietary data, maintaining its own tech stack, and embedding its engineers with customers to deliver deeply integrated solutions that are hard for competitors to copy. Its goal is to give enterprises a competitive edge by deploying production-ready AI agents and models that fit seamlessly into business operations and processes.

Company size
11-50
Company stage
Series C
Total funding
$510M
Headquarters
San Francisco, CA
Founded
2025

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