Overview
AI Research Manager/Scientist, Model Alignment Jobs in Toronto, Canada at Autodesk
Title: AI Research Manager/Scientist, Model Alignment
Company: Autodesk
Location: Toronto, Canada
Category:
Position Overview
As an AI Scientist Manager at Autodesk Research, you will be doing fundamental and applied research that will help our customers imagine, design, and make a better world. We are seeking an AI Scientist Manager to lead our post-training and model alignment efforts. This role sits at the critical intersection of advanced AI research, people leadership, and model readiness. You will both manage and grow a team of AI scientists and personally contribute as a hands-on researcher, owning the transformation of foundation models into reliable, aligned, and production-ready systems.
This is not a purely managerial role. You will remain deeply technical while setting direction, making trade-offs, and taking accountability for model behavior at release.
Autodesk’s AI Lab is active in the wider research community, targeting publications at CVPR, NeurIPS, ICML, ICLR, SIGGRAPH, and other top-tier conferences. We collaborate with top academic & industry labs, combining the best of an academic environment with product-guided research. We are a global team, located in London, San Francisco, Toronto, and remotely in the US, Canada, and Europe.
This role will report to Director of AI Research in the AI Lab.
Responsibilities
Lead and contribute directly to post-training pipelines, including: instruction tuning and multi-task fine-tuning; preference optimization (RLHF, RLAIF, DPO, PPO, and related methods); domain-specific post-training and specialization for the AECO, Manufacturing, and Media & Entertainment industries)
Design and run experiments that shape model behavior, robustness, and reliability
Decide what problems are best addressed through post-training vs pre-training vs product-level mitigation
Partner with infrastructure teams to ensure efficient, reproducible, and scalable post-training workflows
Design and maintain evaluation frameworks that measure: long-horizon reasoning and planning; tool-use and agentic behavior; safety, robustness, and alignment; regression and behavioral drift across releases
Lead human-in-the-loop evaluation, ensuring annotation quality, consistency, and bias awareness
Provide clear go / no-go recommendations for model releases, including explicit articulation of known risks and trade-offs
Manage, mentor, and grow a team of AI scientists working on post-training and alignment
Set clear technical direction while empowering researchers to own end-to-end projects
Hire and develop scientists with strengths across ML, RL, evaluation, and human-centered AI
Foster a culture of: rigorous experimentation and ablation, reproducibility and scientific integrity, thoughtful risk-taking and humility about model behavior
Provide regular feedback, career coaching, and performance management
Act as a key interface between: pre-training research; infrastructure and compute teams;
Model Delivery team; safety, policy, and legal stakeholders
Translate complex research trade-offs into clear, decision-ready guidance for leadership
Influence the broader AI roadmap by identifying post-training opportunities that unlock product impact
Minimum Qualifications
PhD or equivalent industry experience in Machine Learning, AI, or a related field
Proven experience as a people manager of technical research or ML teams
Strong hands-on expertise in: large language models or foundation models, fine-tuning and post-training methods (e.g., RLHF, DPO, instruction tuning), experimental design and evaluation
Ability to move fluidly between research depth and organizational leadership
Strong communication skills, with the ability to explain complex trade-offs to technical and non-technical audiences
Preferred Qualifications
Experience operating in an AI research lab or frontier model organization
Background in human-in-the-loop systems, preference learning, or alignment research
Experience shipping or supporting production AI systems
Familiarity with large-scale training infrastructure and compute cost trade-offs
Experience in Architecture, Civil or Mechanical Engineering, Construction, Manufacturing, Media & Entertainment or other Autodesk domains
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About Autodesk
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