Member of Technical Staff, Recursive Self-Improvement (RSI)

About this role

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 an innovative, rigorous Research Engineer to join our team to accelerate AI self-improvement. This role requires a deep understanding of ML at both the application and system levels. You will ship AI-driven systems that recursively improve how AI systems are trained, evaluated, deployed, and operated at scale. If you are driven by the compounding potential of accelerating the AI loop, you will thrive in this role. Areas you might work on include:

- Build autonomous AI that improves AI. Develop models, harnesses, and pipelines that automate parts of the ML lifecycle - data curation, training optimization, debugging, model selection, and experiment execution - and measure their impact on the speed and reliability of AI R&D.

- Close loops across the stack. Identify the highest-leverage improvement opportunities anywhere in the stack, from distributed pre-training, post-training, and serving to agent harnesses, runtimes, and research environments, and jointly design and optimize across layers.

- Run experiments end-to-end. Form hypotheses, design experiments, build the infrastructure to run them at scale, and turn results into shipped improvements.

- Develop evaluation and observability. Build benchmarks, automated evals, and monitoring systems that surface regressions, failure modes, and emergent behaviors in AI systems.

If you're excited about closing the loop at scale, we'd love to hear from you.

We offer a base salary of $300,000–$500,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.

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