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About the Role

Autodesk offers a distinctive research environment with rich structured data, long-horizon reasoning tasks, and real-world evaluation grounded in professional workflows across architecture, engineering, construction, manufacturing, media & entertainment. Decades of investment in physics simulation engines, CAD kernels, and computational design tools provide high-fidelity, domain-grounded verifiers for post-training, enabling reinforcement learning grounded in physics and engineering constraints. This role offers direct product impact at scale, with research advances translating directly into real-world applications.

Responsibilities

Develop models using post-training techniques, including RLHF, preference optimization, agentic systems, and long-horizon reasoning.
Develop novel algorithms to enhance model reliability, controllability, and alignment.
Make principled architectural decisions regarding challenge resolution at pre-training, post-training, or system levels.
Design and execute experiments to shape model behavior, robustness, and reasoning quality.
Partner with infrastructure teams to build scalable, reproducible post-training workflows.
Contribute to publications, patents, and external research visibility.
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 release recommendations.
Communicate technical risks, limitations, and trade-offs to leadership.

Requirements

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 in academic, AI research, or industry settings.
Strong intuition for model behavior, alignment challenges, and post-training trade-offs.
Experience designing evaluation systems and rigorously assessing model readiness.
Ability to communicate complex technical trade-offs clearly to technical and non-technical audiences.
PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field.
Experience at a frontier model lab or advanced applied AI organization.
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.

Experience Level

Deep hands-on expertise
Proven experience leading or mentoring technical research teams
Equivalent depth of industry research experience to a PhD
Experience at a frontier model lab or advanced applied AI organization
Experience deploying or supporting production AI systems

Education Level

PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field

About the Company

Autodesk is building a diverse workplace and inclusive culture to empower people to imagine, design, and create a better world.
Autodesk software enables the creation of amazing things daily, from green buildings and clean cars to smart factories and hit movies, helping innovators turn ideas into reality and transforming what can be made.
Autodesk's culture is central to its operations, guiding work, interpersonal interactions, customer and partner connections, and global presence, enabling employees to do meaningful work that builds a better world.

Equal Opportunity

Autodesk is an equal opportunity employer, considering all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender, gender identity, national origin, disability, veteran status, or any other legally protected characteristic.
Qualified applicants are also considered regardless of criminal histories, consistent with applicable law.