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Postdoctoral In Reinforcement Learning Jobs in Michigan

University of Michigan

RESEARCH FELLOW

Ann Arbor, MI · On-site

... postdoctoral fellows to undertake projects related to auditory perception, reinforcement learning ... To what extent does neural plasticity in the auditory system occur via mechanisms dictated by ...

FastTek

Machine Learning Engineer

Dearborn, MI

$105.20K - $126.30K/yr

Employees in this job function are responsible for designing, building, deploying, and scaling ... fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning

Stefanini

Machine Learning Engineer

Dearborn, MI

They automate and optimize the end-to-end ML and Gen AI model lifecycle using expertise in ... reinforcement learning from human feedback (RLHF), and instruction tuningDeploy ML models, LLMs ...

Megan soft Inc

AI Engineer (W2 Position)

Dearborn, MI · On-site

$50 - $55/hr

... process automation, reinforcement learning, virtual assistants and specialized programming ... Specialist Exp: 5+ experience in relevant field Skills Required: Artificial Intelligence & Expert ...

General Motors

Senior Perception Engineer

Warren, MI · On-site

$98.10K - $134.70K/yr

... reinforcement learning, and sim2real adaptation. • Design robust training pipelines with high-quality, traceable data for large-scale model development • Validate models in simulation and real ...

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Postdoctoral In Reinforcement Learning information

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 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 popular job titles related to Postdoctoral In Reinforcement Learning jobs in Michigan? For Postdoctoral In Reinforcement Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Michigan look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Michigan are:
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Postdoctoral In Reinforcement Learning jobs near you
Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning

Torc Robotics

Ann Arbor, MI • On-site, Remote

$102.20K - $140.40K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 22 days ago

Job description

About the Company
At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
Meet the Team
As a Senior Machine Learning Engineer - Learned Planner / Reinforcement Learning, you will develop and deploy machine learning models that drive decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will build learned behavior systems that enable safe, efficient, and human-like driving in real-world freight environments.
This role focuses on owning model development and delivery for scoped problem areas, contributing to architecture decisions, and driving improvements in model performance, reliability, and iteration speed within the autonomy stack.
What You'll Do
  • Design, develop, and deploy learned behavior models using approaches such as reinforcement learning, behavior cloning, and imitation learning
  • Own end-to-end model development for scoped problem areas, from data ingestion and training to evaluation and deployment
  • Write production-quality ML code to support scalable training, evaluation, and inference workflows
  • Analyze model performance, identify failure modes, and iterate to improve robustness and generalization across driving scenarios
  • Contribute to training pipelines, data workflows, and infrastructure, including working with large-scale datasets from simulation, fleet logs, and on-vehicle data
  • Collaborate with simulation, validation, and autonomy teams to test and evaluate learned behavior models across diverse environments
  • Support integration of learned planning models into simulation and validation frameworks, enabling faster iteration and improved coverage
  • Contribute to model architecture discussions and technical decision-making within the team
  • Mentor junior engineers on implementation, experimentation, and best practices

What You'll Need to Succeed
  • Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master's degree with 3+ years OR PhD with 1+ years of experience
  • Experience applying reinforcement learning, imitation learning, or sequence modeling to robotics, autonomous systems, or complex control problems
  • Strong programming skills in Python and PyTorch, with experience writing production-quality ML code
  • Experience training, evaluating, and improving models using large-scale datasets and distributed compute environments
  • Solid understanding of ML architectures used in autonomy systems (e.g., transformers, RNNs, graph neural networks, policy networks)
  • Experience debugging model behavior, analyzing performance metrics, and improving model reliability
  • Ability to translate ambiguous problems into structured ML solutions and deliver results independently
  • Experience collaborating cross-functionally to integrate ML models into larger autonomy systems

Bonus Points:
  • Experience in autonomous driving, robotics, or simulation-based training environments
  • Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray)
  • Experience working with simulation environments, scenario generation, or large-scale behavior datasets
  • Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems
  • Experience deploying ML models into production or real-world robotics systems
  • Experience with learned planning systems or policy learning in real-world or simulation environments
  • Experience integrating learned behavior models into validation and V&V workflows
  • Background in multi-agent modeling, driver behavior modeling, or long-horizon decision-making systems

Work Location: For this position, we are open to hiring in either the Ann Arbor, MI OR Blacksburg, VA (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States
Perks of Being a Full-time Torc'r
Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:
  • A competitive compensation package that includes a bonus component and stock options
  • 100% paid medical, dental, and vision premiums for full-time employees
  • 401K plan with a 6% employer match
  • Flexibility in schedule and generous paid vacation (available immediately after start date)
  • Company-wide holiday office closures
  • AD+D and Life Insurance

At Torc, we're committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc'rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.
Even if you don't meet 100% of the qualifications listed for this opportunity, we encourage you to apply.
Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.
Job ID: 102603
Hiring Range for Job Opening
US Pay Range
$226,400-$271,700 USD

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