Research Scientist / Machine Learning Engineer

Job Summary

We are looking for a Research Scientist / Machine Learning Engineer to help build the data engine for frontier AI. This role sits at the intersection of research and engineering, focusing on high-fidelity agentic environments, rigorous evaluations, programmatic verification, and post-training systems that improve model behavior in measurable ways.

Responsibilities

• Build next-generation SimLabs: Design and implement realistic, long-horizon environments where AI agents can reason, act, and maintain context across complex workflows.
• Develop programmatic verification systems: Create policy-aware judges, evaluation stacks, and benchmarks that measure true capability, safety, and reliability beyond simple offline metrics.
• Improve model performance through post-training: Run and iterate on Constant Product Training (CPT), Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), Reinforcement Learning with Human Feedback (RLHF), or Reinforcement Learning with AI Feedback (RLAIF) pipelines using curated, high-signal, and synthetic data.
• Work across the full ML stack: Debug model behavior, data quality, infrastructure bottlenecks, and tooling with a strong command-line-first, developer-friendly mindset.
• Collaborate closely with founders and research staff: Shape the roadmap, experiments, and product direction for frontier AI reliability systems.

Qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Mathematics, Physics, or a related technical field. Strong proof-of-work through open source or impactful industry experience is also valued.
• Strong software engineering fundamentals with the ability to build robust, scalable infrastructure in a Python-friendly environment.
• Deep fluency with foundation models, including training, evaluation, optimization, and deployment.
• Strong experimental and empirical mindset, emphasizing reproducibility, rigorous evaluation, and data-driven iteration.
• Prior experience in an industry research lab or equivalent applied research setting is strongly preferred.

Preferred Skills

• Experience with Reinforcement Learning from Human Feedback (RLHF), Reinforcement Learning with AI Feedback (RLAIF), reward modeling, post-training, red-teaming, or large-model evaluation.
• Experience building simulation systems, agentic environments, or long-horizon tool-using workflows.
• Track record of impactful research through publications at venues such as NeurIPS, ICML, ICLR, ACL, or strong open-source contributions.
• Familiarity with distributed training, ML systems optimization, CUDA, or low-level model/data performance work is a plus.

Career advancement opportunities are not specified.

Benefits

Benefit offerings are not specified.