We are looking for a Member of Technical Staff (MTS - Research) to help us build the data engine for frontier AI. In this role, you will bridge the gap between frontier research and production engineering. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. Your work will involve iterating on novel RL approaches and translating them into robust, scalable infrastructure that moves the needle on real-world model metrics.
Build Agentic Environments: Design and implement the next generation of "SimLabs", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Rapid Iteration: Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain "command-line first" and developer-friendly.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated "proof of work" through significant open-source contributions or industry experience.
Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver "frontier performance" on open-source benchmarks.
