
Member of Technical Staff, Post-Training, RL Infra at Mirendil (San Francisco, CA)
Mirendil· San Francisco, CA·
Role details
Job description
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.
Why work at Mirendil
- Engineering culture: “Small team, singular focus. We hire to compound, not to grow.” The team of ~20 operates with high autonomy and ownership.
- Cutting-edge work: Opportunity to work on one of the most ambitious AI projects – automating the very process of AI research.
- Team composition: Colleagues from Anthropic, xAI, DeepMind, OpenAI; founders known for foundational contributions (SAM optimizer, Blueshift/Mirerva).
- Work environment: Based in San Francisco; likely in-office given the intensity of the work (remote/hybrid policy not publicly detailed).
- Notable perks: Direct exposure to frontier model training and infrastructure; chance to shape a company from its earliest stage.