AI Research Manager​/Scientist, Reinforcement Learning

## AI Research Manager/Scientist, Reinforcement Learning Apply locations:
San Francisco, CA, USA:
AMER
- United States
- Massachusetts
- Boston
- Drydock:
AMER
- United States
- Oregon
- Portland:
California, USA
- Remote:
Boston, MA, USAtime type:
Full time posted on:
Posted 15 Days Agojob requisition :
26WD94883
** Job

Requisition  #
** 26WD94883##

Position Overview As an
** AI Scientist Manager Reinforcement Learning
** 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 odesk'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 Senior Director of AI Research in the AI Lab.## ##

Key Responsibilities ### ### Technical Leadership & Hands-On Research
* 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### ### Evaluation, Alignment &
* Model* Quality
* 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### ### People Management & Team Development
* 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### ### Cross-Functional & Organizational Leadership
* 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## ## Qualifications### ###

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## ## What Success Looks Like
* Post-trained models demonstrate measurable improvements in reliability, alignment, and usefulness
* Evaluation metrics are trusted and adopted across teams
*…