1

Postdoctoral In Reinforcement Learning Jobs in North Carolina

The Postdoctoral Associate will train under the primary mentorship of Dr. Tomi Akinyemiju , with ... learning approaches * Demonstrated experience in scientific writing and publication Ideal for ...

The Postdoctoral Associate will train under the primary mentorship of Dr. Tomi Akinyemiju , with ... learning approaches * Demonstrated experience in scientific writing and publication Ideal for ...

Associate Decision Scientist

Charlotte, NC · On-site

$57K - $58K/yr

Exposure to or coursework in reinforcement learning, multi-armed bandit, or contextual bandit approaches for real-time decisioning is a plus. * Familiarity with cloud-based data environments ...

Exposure to or coursework in reinforcement learning, multi-armed bandit, or contextual bandit approaches for real-time decisioning is a plus. * Familiarity with cloud-based data environments ...

next page

Showing results 1-20

Postdoctoral In Reinforcement Learning information

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Reinforcement Learning, and why are they important?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is a Postdoctoral Researcher in Reinforcement Learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.
What are popular job titles related to Postdoctoral In Reinforcement Learning jobs in North Carolina? For Postdoctoral In Reinforcement Learning jobs in North Carolina, the most frequently searched job titles are:
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in North Carolina look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in North Carolina are:
What cities in North Carolina are hiring for Postdoctoral In Reinforcement Learning jobs? Cities in North Carolina with the most Postdoctoral In Reinforcement Learning job openings:
Infographic showing various Postdoctoral In Reinforcement Learning job openings in North Carolina as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Principal AI Research Scientist Post-Training Alignment Reinforcement Learning Autodesk AI Lab:...

Autodesk

Concord, NC • On-site

Other

Re-posted 24 days ago


Autodesk rating

9.1

Company rating: 9.1 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

26th of 241 rated software companies


Job description

Job Requisition ID #

26WD98667

Position Overview

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.

Respoinsibilities

  • Post-training 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

  • 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

Minimum 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 - 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

  • 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

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. 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, gender identity, national origin, disability, veteran status or any other legally protected characteristic. We also consider for employment all qualified applicants regardless of criminal histories, consistent with applicable law.

Are you an existing contractor or consultant with Autodesk? Please search for open jobs and apply internally (not on this external site). If you have any questions or require support, contact Autodesk Careers.

What Autodesk employees say

Pay

Hours and flexibility

Workplace

Get the full story on Breakroom


Autodesk logo

About Autodesk

Sourced by ZipRecruiter

Autodesk is changing how the world is designed and made. Our technology spans architecture, engineering, construction, product design, manufacturing, media, and entertainment, empowering innovators everywhere to solve challenges big and small. From greener buildings to smarter products to more mesmerizing blockbusters, Autodesk software helps our customers to design and make a better world for all. For more information visit autodesk.com or follow @autodesk.

Industry

Software development

Company size

10,000+ Employees

Headquarters location

San Rafael, CA, US

Year founded

1982