Mercor-posted 4 months ago
San Francisco, CA

As a Software Engineer at Mercor, you’ll join a small, product-driven engineering team building modern backend systems that power critical workflows for some of the most advanced AI companies in the world. You’ll own projects end-to-end, designing, building, and shipping production systems that directly impact customers. From day one you’ll write production-grade code, reason about systems holistically, and make decisions that tie directly to business outcomes. You’ll work closely with product and design, moving fast while upholding a high bar for code quality, scalability, and simplicity. At the end of the process, you’ll be team-matched to where you can have the most impact, on one of the following: Talent platform side – building next-generation tools that automate recruiting end-to-end, intelligently match candidates to roles, and power global hiring with seamless contracts and payments. Applied AI/human data side – partnering with top AI researchers from the AI labs like OpenAI, Anthropic, and Google to create post training datasets to improve foundational model capabilities.

  • Design and build scalable backend APIs, services, and infrastructure.
  • Collaborate across the stack with product and design to ship features quickly.
  • Write clean, maintainable code and contribute to documentation.
  • Participate in code reviews, technical design discussions, and retros.
  • Solve real-world customer problems under real-world constraints.
  • Contribute to core systems that power training, evaluation, and scaling for leading AI labs.
  • Strong coding fundamentals in at least one modern language (Python, Go, Rust, etc.).
  • Understanding of data structures, algorithms, and distributed systems.
  • Familiarity with APIs, SQL/NoSQL databases, and cloud platforms (AWS/GCP).
  • Bias toward ownership, curiosity, and asking the right questions.
  • Ability to break down ambiguity and ship simple solutions to complex problems.
  • Impact: Your work powers how the world’s leading AI labs train and test their models.
  • Learning: Get early insights into frontier model capabilities months before the market.
  • Growth: Work on both infrastructure and research-adjacent projects with fast paths to ownership.
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