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Remote Reinforcement Learning Jobs in Michigan (NOW HIRING)

Remote Reinforcement Learning information

What is a remote reinforcement learning?

A Remote Reinforcement Learning job involves developing and applying reinforcement learning algorithms while working from a location outside of a traditional office environment. Professionals in this field focus on creating systems where agents learn optimal behaviors through trial and error, often using feedback from their environment. These jobs typically require expertise in machine learning, programming, and mathematics, and are commonly found in industries like robotics, gaming, and autonomous systems. Working remotely allows researchers and engineers to collaborate with global teams using digital tools and platforms.

What are the key skills and qualifications needed to thrive as a remote reinforcement learning engineer?

To thrive as a Remote Reinforcement Learning Engineer, you need a strong background in machine learning, statistics, and programming (especially Python), often supported by an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and RL-specific libraries like OpenAI Gym, along with experience using cloud computing platforms, is typically required. Excellent problem-solving skills, self-motivation, and effective remote communication help individuals excel in distributed teams. These skills ensure the successful design, implementation, and deployment of reinforcement learning solutions while collaborating efficiently in a remote work environment.

What are common challenges faced when working remotely in a reinforcement learning role and how can they be addressed?

Working remotely in a Reinforcement Learning role often involves overcoming communication barriers with cross-functional teams, managing large-scale experiments without on-site resources, and staying updated with rapidly evolving research. To address these challenges, it's important to establish regular check-ins with colleagues, utilize cloud-based platforms for experiment management, and participate in virtual seminars or journal clubs. Developing strong self-motivation and time management skills is also crucial to maintain productivity in a remote environment.

What is the difference between Remote Reinforcement Learning vs Remote Machine Learning Engineer?

AspectRemote Reinforcement Learning
Required CredentialsMaster's or PhD in Computer Science, AI, or related fields; knowledge of RL algorithms
Work EnvironmentResearch-focused, experimental, often involves simulation and algorithm development
Employer & Industry UsageTech companies, research labs, AI startups focusing on autonomous systems
Common Search & Comparison IntentUnderstanding specialized AI roles, research focus, and technical skills

Remote Reinforcement Learning specialists focus on developing algorithms that enable machines to learn through trial and error in simulated or real environments. In contrast, Remote Machine Learning Engineers typically work on deploying and optimizing various machine learning models across applications. While both roles require strong programming skills and knowledge of AI, reinforcement learning emphasizes decision-making processes, whereas machine learning engineering covers a broader range of models and deployment strategies.

What are the most commonly searched types of Reinforcement Learning jobs in Michigan?

The most popular types of Reinforcement Learning jobs in Michigan are:

What are popular job titles related to Remote Reinforcement Learning jobs in Michigan?

For Remote Reinforcement Learning jobs in Michigan, the most frequently searched job titles are:

Infographic showing various Remote Reinforcement Learning job openings in Michigan as of August 2026, with employment types broken down into 81% Full Time, and 19% Contract. Highlights an 100% Remote job distribution.

Senior, Machine Learning Engineer - End-to-End

Torc Robotics

Ann Arbor, MI โ€ข On-site, Remote

$119K - $158K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 11 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 - End-to-End (E2E), you will develop and scale learning-based systems that connect multi-modal perception inputs to driving behavior, enabling safe, efficient, and human-like autonomy for real-world freight operations.
You'll work at the intersection of perception, prediction, and planning, contributing to unified learning pipelines that operate in closed-loop environments. This role focuses on owning meaningful portions of the E2E stack, improving model performance at scale, and driving iteration through data, experimentation, and cross-functional collaboration.
This is a hands-on engineering role focused on execution, iteration, and delivery.
What You'll Do
  • Own development and delivery of End-to-End ML models that map multi-modal sensor inputs (camera, LiDAR, radar, maps) to driving-relevant outputs (trajectories, cost functions, or intermediate representations)
  • Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data
  • Analyze model performance, identify failure modes, and drive data-driven improvements in robustness and generalization
  • Design and refine training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality
  • Contribute to model architecture decisions, including approaches such as imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models
  • Collaborate closely with Perception, Prediction, Planning, and Simulation teams to ensure alignment across the autonomy stack
  • Support integration of E2E models into simulation and on-vehicle systems for closed-loop validation
  • Improve tooling, experimentation workflows, and reproducibility across the team
  • Mentor junior engineers and contribute to team-level best practices and technical discussions

What You'll Need to Succeed
  • Bachelor's degree with 6+ years, Master's with 3+ years, or PhD with 1+ years of experience in Machine Learning, Robotics, Computer Science, or a related field with a track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)
  • Experience developing and deploying ML models for autonomous systems, robotics, or complex decision-making environments
  • Strong programming skills in Python and PyTorch, with ability to write production-quality ML code
  • Experience training and evaluating models using large-scale datasets and distributed compute environments
  • Solid understanding of ML architectures used in E2E systems, such as Transformers, BEV models, VLA/VLM approaches, or diffusion models
  • Proven ability to debug model behavior, analyze performance metrics, and drive iterative improvements
  • Experience contributing to or influencing model architecture and training strategies
  • Ability to work cross-functionally and integrate ML systems into larger autonomy pipelines

Bonus Points
  • Experience developing End-to-End or mid-to-end models for autonomous driving or robotics
  • Experience with vision-language models (VLMs) or vision-language-action (VLA) systems
  • Familiarity with closed-loop simulation and evaluation frameworks
  • Experience with reinforcement learning or imitation learning in real-world systems
  • Experience with distributed training frameworks (e.g., Ray)
  • Understanding of vehicle dynamics, motion planning, or multi-agent systems

Work Location: For this position, we are open to hiring in Ann Arbor, MI (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 matchFlexibility 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: 102665
Hiring Range for Job Opening
US Pay Range
$226,400-$271,700 USD