Research Lead / Principal Scientist & Manager Post-Training · Alignment · Reinfo

Job Requisition ID #26WD98298 Research Lead / Principal Scientist & Manager Post-Training · Alignment · Reinforcement Learning Autodesk AI Lab: London · San Francisco · Toronto · Remote (US/CA/EU) Open to Remote: Germany, France or Italy The Opportunity Foundation models are reshaping how engineers, architects, and designers work — but training foundation models that are reliable, domain-capable systems is still an open research problem. Autodesk touches more of the physical world than almost any other software company. The products we build are used to design skyscrapers, manufacture aircraft, and produce films. AI is now central to how those workflows are evolving — and post-training is the layer that makes the difference between a capable model and one that is dependable and robust in our customers’ high-precision domains. As Research Lead for Post-Training & Alignment, you will own Autodesk's research strategy for transforming foundation models into systems that are reliable, aligned, and genuinely useful in complex, domain-specific workflows. This is a deeply technical leadership role — you will shape research direction, drive key architectural decisions, and remain close to the work. You will lead a growing team of AI scientists while continuing to contribute directly to research: running experiments, developing novel algorithms, and publishing at top-tier venues. This role reports to the Senior Director of AI Research within Autodesk AI Lab. Why This Role Unique research surface area 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. Research-first, with real impact 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. What You Will Do 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 Contribute to publications, patents, and Autodesk's external research visibility Evaluation & Model Quality 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 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 Help recruit world-class talent across ML, RL, alignment, and foundation models Partner closely with pre-training teams, infrastructure, product organizations, and other stakeholders Translate research trade-offs into clear, decision-ready guidance for leadership What We Are Looking For We care about research judgment and outcomes, not credential checklists. Strong candidates will typically have: 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 techn