AI Research Scientist - Early Career (PHD/MS)

What you'll do

• Develop research questions, methods, evaluations, and experiments for agentic capabilities, reliability, and scaling.
• Implement and integrate research methods, datasets, environments, verifiers, and evaluations into training and production systems.
• Analyze model behavior, communicate findings, and collaborate with research and engineering teams to advance the roadmap.

What they ask for

• Completing or recently completed a PhD in a relevant field, or an equivalent record of original, technically rigorous research.
• Strong machine-learning, reinforcement-learning, LLM, agents, or ML-systems research record, demonstrated through publications, preprints, open-source work, or substantial independent projects.
• Excellent Python skills and hands-on experience with a modern deep-learning framework such as PyTorch or JAX.
• Experimental rigor and engineering ability to build research infrastructure and turn ideas into working systems.

Nice to have

• Experience in agentic reinforcement learning, post-training, preference learning, reward or verifier modeling, evaluation, or environment design.
• Experience with large-model training, distributed inference, high-throughput rollout systems, or performance-sensitive ML infrastructure.

Tools

Python, PyTorch, JAX

AI focus

Research and develop agentic reinforcement learning, post-training, model evaluation, environments, verifiers, and scaling methods; translate results into improved models and production systems.