Join Mirendil as a staff engineer to build the post-training stack for frontier reasoning models in a tech-first startup.
Posted by employer 4 months ago
First seen on Joblaze 2 months ago
Last verified on the company career page 22 hours ago
Skills & Technologies
Not disclosed in this posting: years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the engineer focuses on developing and enhancing the post-training infrastructure for reinforcement learning models, ensuring reliability and performance at scale. Key skills include expertise in large-scale RL training, performance optimization, and data pipeline development. This position is ideal for experienced engineers with a strong background in both research and infrastructure, looking to contribute to cutting-edge AI advancements. Mirendil's commitment to democratizing AI R&D creates a dynamic environment for innovation and collaboration.
Joblaze insights
- Listed about 2 months ago — first seen on Joblaze September 3, 2026. Last confirmed on Mirendil's careers page November 12, 2026.
- Salary band is above the typical range for AI/ML roles (median ~$188,000).
- Starts above 65% of 63 comparable staff ai/ml roles that list AI/ML we track (median $245,402 across 24 companies). See AI/ML salary trends
- AI/ML appears in 34% of 444 comparable staff ai/ml roles; reinforcement learning appears in 5% of 444 comparable staff ai/ml roles.
Quick facts
- What's the salary range?
- Mirendil lists $300,000–$400,000 for this role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, reinforcement learning.
- What seniority level is this role?
- Mirendil targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff, Post-Training, RL Infra role at Mirendil.
From the original posting
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.
Standard company text repeated across Mirendil's postings is omitted here.