Director, Data Science
Ann Arbor, MI · On-site
... ADAS, robotics, large-scale computer vision systems, simulation and synthetic data, reinforcement learning, or large-scale ML platforms. • Demonstrated track record leading a team of 10 or more ...
Director, Data Science
Ann Arbor, MI · On-site
... ADAS, robotics, large-scale computer vision systems, simulation and synthetic data, reinforcement learning, or large-scale ML platforms. • Demonstrated track record leading a team of 10 or more ...
... learning, reinforcement learning, numerical analysis, statistics, robotics, or distributed computing. 5. Proficient in Linux or other Unix-based operating system. 6. Experience leading software ...
... learning, reinforcement learning, numerical analysis, statistics, robotics, or distributed computing. 5. Proficient in Linux or other Unix-based operating system. 6. Experience leading software ...
Research Engineer III - Software Developer
Ann Arbor, MI · On-site
$200K/yr
... learning, reinforcement learning, numerical analysis, statistics, robotics, or distributed computing. 5. Proficient in Linux or other Unix-based operating system. 6. Experience leading software ...
Research Engineer III - Software Developer
Ann Arbor, MI · On-site
$200K/yr
... learning, reinforcement learning, numerical analysis, statistics, robotics, or distributed computing. 5. Proficient in Linux or other Unix-based operating system. 6. Experience leading software ...
Practice Manager - AI & Data
Troy, MI · On-site
$160K - $190K/yr
... devices, robotics, automotive, commercial vehicles, EVs, rail, and more. As part of the global ... Machine Learning & Deep Learning (supervised, unsupervised, reinforcement learning) * Support ...
Quick apply
Practice Manager - AI & Data
Troy, MI · On-site
$160K - $190K/yr
... devices, robotics, automotive, commercial vehicles, EVs, rail, and more. As part of the global ... Machine Learning & Deep Learning (supervised, unsupervised, reinforcement learning) * Support ...
Practice Manager - AI & Data
Troy, MI · On-site
$160K - $190K/yr
... devices, robotics, automotive, commercial vehicles, EVs, rail, and more. As part of the global ... Machine Learning & Deep Learning (supervised, unsupervised, reinforcement learning) * Support ...
Practice Manager - AI & Data
Troy, MI · On-site
$160K - $190K/yr
... devices, robotics, automotive, commercial vehicles, EVs, rail, and more. As part of the global ... Machine Learning & Deep Learning (supervised, unsupervised, reinforcement learning) * Support ...
Sr. Data Engineer
Ann Arbor, MI · On-site
$140K - $200K/yr
Our internal platform, PlantOS, uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots-but applied to autonomous, short-interval control of mineral ...
Sr. Data Engineer
Ann Arbor, MI · On-site
$140K - $200K/yr
Our internal platform, PlantOS, uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots-but applied to autonomous, short-interval control of mineral ...
Reinforcement Learning Robotics information
What is reinforcement learning in robotics?
Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.
What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?
One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.
What are the key skills and qualifications needed to thrive as a reinforcement learning robotics engineer, and why are they important?
To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.
What is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?
| Aspect | Reinforcement Learning Robotics | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Degree in Robotics, Computer Science, or related fields; knowledge of reinforcement learning | Degree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms |
| Work Environment | Robotics labs, manufacturing, autonomous systems | Tech companies, data-driven projects, software development |
| Industry Usage | Autonomous robots, industrial automation, research | Data analysis, predictive modeling, AI applications |
Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.
What are popular job titles related to Reinforcement Learning Robotics jobs in Michigan?
For Reinforcement Learning Robotics jobs in Michigan, the most frequently searched job titles are:
- Embedded Machine Learning Engineer
- Machine Learning Engineer New Grad
- Senior Machine Learning Researcher
- Machine Learning Engineer Intern
- Remote Machine Learning Engineer
- Junior Machine Learning Engineer
- Entry Level Machine Learning Engineer
- Machine Learning Engineer Apprenticeship
- Google Cloud Machine Learning Engineer
What job categories do people searching Reinforcement Learning Robotics jobs in Michigan look for?
The top searched job categories for Reinforcement Learning Robotics jobs in Michigan are:
- Junior Machine Learning
- No Experience Nvidia Machine Learning
- Deep Learning Ai
- Director Google Machine Learning Engineer
- Contract Apple Machine Learning Engineer
- Freelance Machine Learning Compiler Engineer
- Artificial Intelligence Machine Learning Engineer
- Deep Learning Quantization
- Flexible Teen Robotics Engineer
- Machine Learning Robotics
What cities in Michigan are hiring for Reinforcement Learning Robotics jobs?
Cities in Michigan with the most Reinforcement Learning Robotics job openings:
Director, Data Science
Ann Arbor, MI • On-site
May Mobility •
Urban Transit Systems • 11 - 50 employees11 - 50 employees
Full-time
Re-posted 14 days ago
Job description
Job Summary:
May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. The Director, Data Science will lead the team responsible for turning data generated by the fleet into insights that enhance the safety and efficiency of their autonomous services while collaborating with various departments to set standards and translate operational data into actionable strategies.
Responsibilities:
• Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.
• Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.
• Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.
• Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.
• Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.
• Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.
• Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.
• Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.
• Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.
Qualifications:
Required:
• 8+ years of industry experience in data science, machine learning, or applied research, with at least 4 years managing senior individual contributors and front-line managers.
• Direct experience leading data science or ML work in at least one of the following domains: autonomous vehicles or ADAS, robotics, large-scale computer vision systems, simulation and synthetic data, reinforcement learning, or large-scale ML platforms.
• Demonstrated track record leading a team of 10 or more through a major delivery — for example, a production launch, a major model rollout, a regulatory milestone, or a significant ODD or product expansion.
• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Physics, Robotics, or a related quantitative field, or equivalent practical experience.
• Strong programming skills in Python; working familiarity with the production ML stack used in modern AV/robotics environments (e.g., PyTorch or TensorFlow, distributed training, dataset and feature pipelines, experiment tracking).
• Experience setting measurement and experimentation standards inside an engineering or product organization, with credible examples of metrics or evaluation frameworks the team adopted and kept using.
• Experience operating in cross-functional partnership with engineering, product, safety, and operations leaders — comfortable both defending technical positions and adjusting them in light of business or safety constraints.
Preferred:
• Master's or PhD in Computer Science, Robotics, Statistics, EE, Mathematics, or a related quantitative field.
• Prior experience at an autonomous vehicle, robotics, or hard-tech company that has deployed products to real customers (not only research demos).
• Experience with simulation, synthetic data generation, sim-to-real transfer, or scenario-based evaluation for AV or robotics.
• Familiarity with safety-case construction, ODD definition, or regulator engagement for autonomous systems.
• Publications or conference contributions in top-tier ML, CV, or robotics venues (e.g., NeurIPS, ICML, CVPR, ICRA, RSS).
• Experience with C/C++ systems and/or GPU programming sufficient to engage credibly with onboard ML and infrastructure teams.
• Demonstrated ability to mentor and grow junior managers and senior individual contributors into bigger roles.
Company:
May Mobility is a manufacturing firm that designs and develops autonomous technology vehicles for self-driving transportation solutions. Founded in 2017, the company is headquartered in Ann Arbor, USA, with a team of 201-500 employees. The company is currently Growth Stage.
May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. The Director, Data Science will lead the team responsible for turning data generated by the fleet into insights that enhance the safety and efficiency of their autonomous services while collaborating with various departments to set standards and translate operational data into actionable strategies.
Responsibilities:
• Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.
• Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.
• Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.
• Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.
• Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.
• Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.
• Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.
• Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.
• Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.
Qualifications:
Required:
• 8+ years of industry experience in data science, machine learning, or applied research, with at least 4 years managing senior individual contributors and front-line managers.
• Direct experience leading data science or ML work in at least one of the following domains: autonomous vehicles or ADAS, robotics, large-scale computer vision systems, simulation and synthetic data, reinforcement learning, or large-scale ML platforms.
• Demonstrated track record leading a team of 10 or more through a major delivery — for example, a production launch, a major model rollout, a regulatory milestone, or a significant ODD or product expansion.
• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Physics, Robotics, or a related quantitative field, or equivalent practical experience.
• Strong programming skills in Python; working familiarity with the production ML stack used in modern AV/robotics environments (e.g., PyTorch or TensorFlow, distributed training, dataset and feature pipelines, experiment tracking).
• Experience setting measurement and experimentation standards inside an engineering or product organization, with credible examples of metrics or evaluation frameworks the team adopted and kept using.
• Experience operating in cross-functional partnership with engineering, product, safety, and operations leaders — comfortable both defending technical positions and adjusting them in light of business or safety constraints.
Preferred:
• Master's or PhD in Computer Science, Robotics, Statistics, EE, Mathematics, or a related quantitative field.
• Prior experience at an autonomous vehicle, robotics, or hard-tech company that has deployed products to real customers (not only research demos).
• Experience with simulation, synthetic data generation, sim-to-real transfer, or scenario-based evaluation for AV or robotics.
• Familiarity with safety-case construction, ODD definition, or regulator engagement for autonomous systems.
• Publications or conference contributions in top-tier ML, CV, or robotics venues (e.g., NeurIPS, ICML, CVPR, ICRA, RSS).
• Experience with C/C++ systems and/or GPU programming sufficient to engage credibly with onboard ML and infrastructure teams.
• Demonstrated ability to mentor and grow junior managers and senior individual contributors into bigger roles.
Company:
May Mobility is a manufacturing firm that designs and develops autonomous technology vehicles for self-driving transportation solutions. Founded in 2017, the company is headquartered in Ann Arbor, USA, with a team of 201-500 employees. The company is currently Growth Stage.
About May Mobility
Sourced by ZipRecruiter
Industry
Urban transit systems
Company size
11 - 50 Employees
Headquarters location
Ann Arbor, MI, US
Year founded
2017