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 build AI that makes AI systems better. This role requires a deep understanding of ML at both the application and system levels. You will ship AI-driven systems that improve how AI systems are trained, evaluated, deployed, and operated. If you're energized by closing the AI loop, this role is for you. Areas you might work on include:
- Build AI that improves AI. Develop models, agents, and pipelines that automate and enhance parts of the ML lifecycle: data curation, training optimization, evaluation, debugging, model selection, and deployment.
- Run experiments end-to-end. Form hypotheses, design experiments, build the infrastructure to run them at scale, and turn results into shipped improvements.
- Optimize training and inference systems. Profile and improve throughput, memory, and cost across distributed training and serving, and feed those learnings back into automated tooling.
- Develop evaluation and observability. Create benchmarks, automated evals, and monitoring that surface model regressions, failure modes, and emergent behaviors.
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.