Drimva

Institute of Foundation Models

Developer Relations Manager

$200K–$250K

Sunnyvale, CA · On-site · Manager · Full-time · Posted Oct 22, 2026

Education
Bachelor’s degree in Computer Science, Engineering or a related technical field, or equivalent practical experience.
Visa sponsorship
Eligible for visa sponsorship.

About the job

Drives developer adoption of IFM’s LLMs by helping builders evaluate, integrate, and use them, while relaying evidence-based feedback to research and engineering teams.

What you'll do

  • Support developers throughout discovery, evaluation, integration, and ongoing use; develop documentation, guidance, and tested examples.
  • Demonstrate model capabilities and limitations, and collaborate with ecosystem partners to enable reliable integrations.
  • Investigate adoption barriers, reproduce issues, and work with engineering and research teams to validate improvements and inform priorities.

What they ask for

  • Experience in developer relations, applied AI engineering, developer experience, or a comparable technical role, with a track record of helping developers adopt technical products.
  • Hands-on experience building, evaluating, and deploying LLM applications; judgment about model capabilities, limitations, and quality, latency, and cost trade-offs.
  • Strong Python skills and a portfolio of technical documentation, tutorials, demonstrations, or open-source contributions.
  • Experience collaborating with researchers and engineers and familiarity with the LLM developer ecosystem, including inference tools, application frameworks, and agent workflows.

Nice to have

  • Contributing to model launches, early-access programs, or open-source integrations.
  • Experience with open-weight models, fine-tuning or self-hosted deployment, or creating guidance for coding agents.

Pay

USD 200,000–250,000 per year (base salary); discretionary performance bonus (amount not stated)

Tools

Python

AI focus

Supports adoption and evaluation of LLMs, develops examples for tool calling, structured outputs, and multi-step workflows, and translates reproducible findings into model improvement recommendations.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)