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

Research & Technical Leadership

Own post-training strategy 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

Team & Organizational Leadership

Manage, mentor, and grow a team of AI scientists
Set technical direction and research priorities across post-training and alignment initiatives
Foster a research culture grounded in scientific rigor, reproducibility, and fast iteration
Partner closely with pre-training teams, infrastructure, product organizations, and other stakeholders
Translate research trade-offs into clear, decision-ready guidance for leadership

Why This Role

Unique research surface area
Uniquely, decades of investment in physics simulation engines, CAD kernels, and computational design tools give us something most labs don't have: high-fidelity, domain-grounded verifiers that can serve as reward signals for post-training.

What You Will Do

Research & Technical Leadership
Post-trained models show measurable improvements in reliability, alignment, reasoning quality, and domain usefulness
Evaluation metrics and release criteria are trusted and adopted across teams
Deliver high-quality research with practical impact — and team members are growing into stronger, more independent researchers
Leadership relies on your judgment for model readiness, technical direction, and risk assessment
Autodesk AI Lab advances its reputation as a serious contributor to frontier AI research

Qualifications

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
Ability 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