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

Research Lead / Principal Scientist & Manager, Post-Training Alignment Reinforcement Learning

Autodesk AI Lab advances state-of-the-art research across generative AI, multimodal foundation models, reasoning systems, and human-AI collaboration. Our work has direct impact across the industries that shape the physical world. We are an active contributor to the global research community and collaborate closely with leading academic and industry labs.

At Autodesk, we are building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world. Autodesk is proud to be an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected characteristic.

Position Overview

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.

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.

This role reports to the Senior Director of AI Research within Autodesk AI Lab.

Responsibilities

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

Minimum Qualifications

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 technical and non-technical audiences
• A PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field

Preferred Qualifications

• 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

The Ideal Candidate

In The First Year, Success Means

• 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
• The team delivers 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

Key skills/competency

• Reinforcement Learning
• Foundation Models
• AI Alignment
• Post-Training AI
• Research Leadership
• Algorithm Development
• Model Evaluation
• Team Management
• Machine Learning
• Deep Learning