Senior Applied Scientist, AI Platform

What you'll do

• Establish the applied science methodology and technical roadmap for building simulated environments and post-training data.
• Define and measure the quality of post-training data regarding correctness, representativeness, and difficulty.
• Research and engineer ways to make simulated environments mimic imperfect, real-world production systems.
• Build scalable, production-grade systems for synthetic environments rather than just research scripts.
• Determine how to apply generated data in LLM post-training and define evaluation approaches for agent trajectories.
• Develop methods to evaluate agent performance within non-deterministic environments.
• Collaborate with cross-functional teams to integrate findings into model training and evaluation pipelines.

What we're looking for

• You must have a PhD, MS, or equivalent research experience in a scientific field with strong applied mathematics grounding.
• You must have 6+ years of relevant applied science or ML engineering experience, including setting technical direction for others.
• You must have hands-on experience with LLM and agent post-training data creation, management, and quality control.
• You must possess domain expertise in LLMs and agentic applications rather than classical ML fine-tuning.
• You must have experience evaluating agents or LLM applications and defining success metrics for them.
• You must be a strong programmer capable of building scalable production systems using Python and distributed systems.
• You should have a background in statistics, experiment design, data analysis, and deploying production-level ML infrastructure.
• Experience with observability or monitoring systems and an architecture-level understanding of LLMs is preferred.