Member of Technical Staff - Machine Learning Capabilities, New Graduates
About the role
About Us Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.
About
the Role We’re hiring new graduate Machine Learning Engineers to design and build reinforcement learning environments to safely advance model capabilities specifically on machine learning research and engineering tasks to do the work of an MLE at a frontier lab. This role blends research and engineering . It will require you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership and autonomy of the environments you build. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers and engineers. You will join our Capabilities org, a small, high-ownership team and contribute directly to the data layer that powers frontier LLM capability. What You Will Do: Design and build RL environments and reward schemes that produce clean, learnable signals for frontier models on ML research and engineering tasks. Build deep expertise across the frontier of ML research, training, and inference infrastructure. Collaborate with others to brainstorm and create new ideas and tools to improve the environment building process. What We are Looking For (
Qualifications): You have strong ML fundamentals and broad research interests. You read many papers or tutorials, understand topics deeply and have the creativity to translate them into RLVR problems. Proficiency in Python and systems programming; ideally PyTorch or JAX Smart problem solvers who take ownership and drives solutions end-to-end Passion for staying current with the rapidly evolving ML infrastructure landscape Ability to meet throughput expectations and respond quickly to feedback
