Join Mirendil as a research engineer to build data systems and execution environments for reinforcement learning.
Posted by employer 2 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 13 hours ago
Skills & Technologies
Not disclosed in this posting: years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the research engineer is responsible for developing and maintaining the data systems and execution environments essential for reinforcement learning at Mirendil. Key skills include expertise in building data collection pipelines, creating secure execution environments, and collaborating across teams to enhance model performance. This position is ideal for someone with a strong background in AI research and engineering, particularly at a senior level. Mirendil's focus on democratizing AI R&D suggests a dynamic environment where innovation is prioritized.
Joblaze insights
- Listed 12 days ago on Joblaze — tracked directly from Mirendil's career page.
- Mirendil has 11 other open roles including Executive Assistant & Operations , Member of Technical Staff, Recursive Self-Improvement (RSI), Member of Technical Staff, Kernels.
- 133 active reinforcement learning roles on Joblaze right now.
- 17 active data systems roles on Joblaze right now.
- Salary band is above the typical range for AI/ML roles (median ~$188,000).
- Pays more than 59% of the 61 salaried reinforcement learning roles we track (median $228,800).
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: data systems, execution environments, 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 Environments role at Mirendil.
From the original posting
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 a research engineer to build the data systems and execution environments that power reinforcement learning at Mirendil. The quality of our models depends directly on the quality of the data and environments we train on; you will own those systems end-to-end. Some example areas you might work on (not limited to):
Build and automate data collection pipelines for complex, long-horizon RL tasks.
Build robust systems to identify and prevent reward hacking.
Build scalable sandboxed execution environments for realistic tasks involving potentially multiple agents, nodes, and users.
Design systems to estimate the influence of training environments on production model behavior.
Collaborate with teams across the stack to identify potential axes of improvements in production model behavior, and develop training environments to push these axes.
If you're excited about building the data and environment infrastructure that determine what our models learn, 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.