Requirements:

• PhD, MS, or equivalent research experience in a scientific field with strong applied mathematics grounding
• 6+ years of relevant applied science or ML engineering experience, including setting technical direction for others
• Hands-on experience with LLM and agent post-training data
• Real domain expertise in LLMs and agentic applications
• Strong programming and production software engineering skills
• Collaborative across engineering and science teams
• Comfortable making technical decisions in ambiguous situations

Nice to Haves:

• Hands-on LLM fine-tuning, post-training, or model training experience
• Background in statistics, experiment design, and data analysis
• Experience deploying production-level ML infrastructure
• Observability or monitoring systems background
• Architecture-level understanding of LLMs

What You'll Be Doing:

• Own the applied science direction for GenSim
• Define and measure the quality of post-training data
• Close the realism gap in simulated environments
• Build scalable, production-grade systems
• Determine how data is best applied in LLM post-training and evaluation
• Work cross-functionally with adjacent engineering and science teams

Perks & Benefits:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intra-departmental mentor and buddy program for in-house networking
• An inclusive company culture and the ability to join our Community Guilds
• Access to Inclusion Talks, our internal panel discussions
• Free, global Spring Health benefits for employees and dependents age 6+
• Competitive global benefits