About the role
About the Role
As a Research Engineer at Metis, you’ll work on building the next generation of autonomous post-training systems that leverage our Mantis platform. You’ll operate at the intersection of cutting-edge ML research and scalable engineering, designing, implementing, and deploying algorithms that improve how AI agents learn from feedback, synthetic data, and real-world interactions.
You’ll move seamlessly between papers and production, leading large-scale experiments, creating optimized training pipelines, and helping shape the future of post-training autonomy. You’ll have significant ownership, high compute budgets, and the mandate to push the state of the art in applied reinforcement and preference optimization.
What You'll Do
- Research and help build an autonomous post-training agent leveraging the Mantis platform
- Design and execute large-scale experiments on synthetic data generation and algorithmic architecture
- Develop and refine methods for reinforcement learning, reward modeling, and human feedback integration
- Collaborate cross-functionally with Core and Platform Engineering to deploy and evaluate models in production settings
- Publish or contribute to leading-edge research in the post-training domain
- Use tooling and compute efficiently to iterate on experimental pipelines and accelerate research velocity
Requirements
- Deep experience in machine learning, preferably reinforcement learning, post-training, or alignment research
- Demonstrated research contributions; ideally published papers (ICML, NeurIPS) or public implementations
- Strong proficiency in Python and ML frameworks (PyTorch, JAX, or TensorFlow)
