Principal AI Research Scientist Post-Training · Alignment · Reinforcement Learning Autodesk AI Lab: London · San Francisco · Toronto · Remote (US/CA/EU

Resposibilities

• Post-training for model development — from RLHF and preference optimization to agentic systems and long-horizon reasoning
• Develop novel algorithms that improve model reliability, controllability, and alignment
• Make principled architectural decisions about when to address challenges at the pre-training, post-training, or system level
• Design and run experiments that shape model behavior, robustness, and reasoning quality
• Partner with infrastructure teams to build scalable, reproducible post-training workflows
• Contribute to publications, patents, and Autodesk's external research visibility
• Design evaluation frameworks for long-horizon reasoning, tool use, agentic behavior, safety, and real-world workflow completion
• Drive human-in-the-loop evaluation with high annotation quality and sound scientific methodology
• Establish model readiness criteria and provide go/no-go recommendations for releases
• Communicate technical risks, limitations, and trade-offs clearly to leadership

Minimum Requirements

• Deep hands-on expertise in reinforcement learning for foundation models, and fluency with post-training methods (RLHF, RLAIF, DPO, PPO, or adjacent approaches)
• Proven experience leading or mentoring technical research teams — whether in an academic lab, AI research organization, or industry setting
• Strong intuition for model behavior, alignment challenges, and post-training trade-offs
• Experience designing evaluation systems and thinking rigorously about what it means for a model to be ready
• Able to communicate complex technical trade-offs clearly to both technical and non-technical audiences
• A PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field
• Experience at a frontier model lab or advanced applied AI organization
• A strong publication record at leading ML or AI venues
• Background in alignment research, preference learning, or agentic AI
• Experience deploying or supporting production AI systems
• Familiarity with large-scale training infrastructure and compute trade-offs