Research Lead / Principal Scientist & Manager Post-Training · Alignment · Reinforcement Learning Autodesk AI Lab: London · San Francisco · Toronto · Remote (US/CA/EU)
Job
Autodesk
Portland, OR (In Person)
$268,725 Salary, Full-Time
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Job Description
- Job Requisition ID #
- 26WD94883
- Research Lead / Principal Scientist & Manager
- Post-Training
- Alignment
Reinforcement Learning Autodesk AI Lab:
London- San Francisco
- Toronto
- Remote (US/CA/EU)
- 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.
- 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.
- 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 technical and non-technical audiences + A PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field We also value, but do not require: + 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- What Success Looks Like
- 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 •About Autodesk AI Lab•Autodesk AI Lab advances state-of-the-art research across generative AI, multimodal foundation models, reasoning systems, and human-AI collaboration.
- Learn More
- About Autodesk
- Welcome to Autodesk!
- Benefits
- From health and financial benefits to time away and everyday wellness, we give Autodeskers the best, so they can do their best work.
by visiting https:
//benefits.autodesk.com/- Salary transparency
- Salary is one part of Autodesk's competitive compensation package.
- Equal Employment Opportunity
- At Autodesk, we're building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world.
- Diversity & Belonging
- We take pride in cultivating a culture of belonging where everyone can thrive.
Learn more here:
https://www.autodesk.com/company/diversity-and-belonging- Are you an existing contractor or consultant with Autodesk?
- Please search for open jobs and apply internally (not on this external site).
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