Position Overview
Autodesk's domains — architecture, engineering, construction, manufacturing, media & entertainment — provide a distinctive research environment: rich structured data, long-horizon reasoning tasks, and real-world evaluation grounded in professional workflows. 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. Rather than relying solely on human preference data, we can ground reinforcement learning in the laws of physics and the constraints of real engineering. These are exactly the kinds of challenges — and assets — that make post-training and alignment research here genuinely distinctive.
We publish at NeurIPS, ICML, ICLR, CVPR, and SIGGRAPH. We collaborate with leading academic and industry labs. And we have a direct line from research advances to product impact at scale. This is not a role where research sits behind a wall from engineering — you will see your work matter.
Respoinsibilities
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
Lead rigorous model analysis and interpretability efforts
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
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
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
Principal AI Research Scientist Post-Training · Alignment · Reinforcement Learning Autodesk AI Lab: London · San Francisco · Toronto · Remote (US/CA/EU • Bentall Centre, Vancouver, British Columbia, Canada
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