Level

MirendilPosted 3mo ago

L4

Member of Technical Staff, Post-Training, RL Infra

Member of Technical Staff, Post-Training, RL Infra at Mirendil scores 98 out of 100 on AI centrality, which makes it a Level 4 role on this board.

San FranciscoleadFullTime$300k

AI in this role

ai-research

Mirendil

Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.

The Role

We are looking for engineers to help build the post-training stack for frontier reasoning models. This role sits at the intersection of research and infrastructure. You will work to push the scale of our RL stack, whether it is novel recipe ideas, reliability, or performance. Some example areas you might work on (not limited to):

  • Design and build reliable infrastructure for large-scale RL training

  • Implement novel performance optimizations across the training stack

  • Develop evaluation and benchmarking infrastructure to measure model progress, throughput, and uptime

  • Build data collection and feedback pipelines that close the loop between human signal, reward modeling, and training

  • Collaborate with multiple teams to rapidly iterate on RL algorithms and get experiments into production training runs

If you're excited about building the infrastructure that makes frontier RL research possible at scale, we'd love to hear from you.

We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.

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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI Research

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. How would you decide a model or AI system is ready to ship?
  3. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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  • List these exact terms on your resume: AI Research. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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