Member of Technical Staff, Post-Training, RL Environments

Mirendil • Full-time • United States, California • 2w ago

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 believe accelerating scientific discovery is one of the most powerful ways to improve the future of humanity, and that AI will play a central role in making that possible.

We are building a frontier AI research company and training our own models end-to-end. Our work spans areas such as model training, reinforcement learning, reasoning systems, and infrastructure for large-scale experiments. Our team includes researchers and engineers from Anthropic, Google DeepMind, xAI, OpenAI, Microsoft, Apple, and MIT.

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 $350,000–$500,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.