... robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and ...
... robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and ...
Senior Applied Scientist, Safe Locomotion, Compass
Pasadena, CA · On-site
$99K - $136K/yr
This is a rare opportunity to shape how legged robots move through the world alongside people. Key job responsibilities • Design, train, and deploy reinforcement learning policies for dynamic ...
Senior Applied Scientist, Safe Locomotion, Compass
Pasadena, CA · On-site
$99K - $136K/yr
This is a rare opportunity to shape how legged robots move through the world alongside people. Key job responsibilities • Design, train, and deploy reinforcement learning policies for dynamic ...
ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic ... reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in ...
ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic ... reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in ...
ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic ... reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in ...
ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic ... reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in ...
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Quick apply
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Humanoid Engineer, Manipulation
Irvine, CA · On-site
$100 - $125/hr
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Humanoid Engineer, Manipulation
Irvine, CA · On-site
$100 - $125/hr
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation * You ...
Senior Software Engineer, Controls
Los Angeles, CA · On-site
$150K - $250K/yr
Getting the robot walking reliably and integrating magnetic adhesion for vertical/near-vertical ... Reinforcement Learning * Sim-to-Real Transfer The annual base salary range for this Palo Alto ...
New
Senior Software Engineer, Controls
Los Angeles, CA · On-site
$150K - $250K/yr
Getting the robot walking reliably and integrating magnetic adhesion for vertical/near-vertical ... Reinforcement Learning * Sim-to-Real Transfer The annual base salary range for this Palo Alto ...
New
... robotics. We identify key technology gaps requiring step-change innovation, build AI-driven ... Design and implement reinforcement learning and alignment strategies (e.g., physics-informed reward ...
... robotics. We identify key technology gaps requiring step-change innovation, build AI-driven ... Design and implement reinforcement learning and alignment strategies (e.g., physics-informed reward ...
Principal Research Scientist - Frontier Industrial AI
Lake Forest, CA · Hybrid
$213K - $355K/yr
... generative models, reinforcement learning, agentic systems, simulation, world models ... Recent publications at leading machine learning, AI, data mining, robotics, or systems venues.
Principal Research Scientist - Frontier Industrial AI
Lake Forest, CA · Hybrid
$213K - $355K/yr
... generative models, reinforcement learning, agentic systems, simulation, world models ... Recent publications at leading machine learning, AI, data mining, robotics, or systems venues.
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 Fullerton, CA?
For Reinforcement Learning Robotics jobs in Fullerton, CA, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Robotics jobs in Fullerton, CA look for?
The top searched job categories for Reinforcement Learning Robotics jobs in Fullerton, CA are:
What cities near Fullerton, CA are hiring for Reinforcement Learning Robotics jobs?
Cities near Fullerton, CA with the most Reinforcement Learning Robotics job openings:
Full-time
Medical, Dental, Vision, Life, Retirement, PTO
Re-posted 15 days ago
Key responsibilities
Perform research on safe autonomy, including theory, integration with learning, and synthesis from perception.
Develop simulation and evaluation pipelines for large-scale validation of methods in high fidelity simulation environments.
Deploy developed methods on hardware, focusing on dynamically stable robots, and validate their effectiveness in practice.
Amazon rating
7.4
Based on 7,154 frontline employees who took The Breakroom Quiz
5th of 39 rated national retailers
Job description
Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale
We are seeking a Postdoctoral Scholar to join the SAF Lab. In this role, you will perform research around safe autonomy on highly dynamic robots, with a special focus on loco-manipulation and dynamically stable robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and robust whole-body control, layered safety filters that interface with learning modules, and the synthesis of CBFs from perception data and semantic information. You will push the boundaries of safe autonomy and validate your discoveries experimentally on the next generation of robotic platforms.
The SAF lab provides a unique opportunity to collaborate with the inventor of CBFs, top scientists and engineers at Amazon developing the next generation of safe autonomy, while also establishing strong connections with top academic research labs. Your research in the SAF lab will lay the foundations of safe learning on complex robots - removing bottlenecks to deployment and enable them to safely operate around humans.
Key job responsibilities
In this role you will:
• Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains.
• Develop the simulation and evaluation pipelines needed to run complex and large-scale validation of methods developed in high fidelity simulation environments.
• Develop sim-to-real transfer pipelines that enable the deployment of simulation-based methods (controllers, policies) on hardware.
• Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research.
• Publish research at top-tier robotics, control and ML venues and contribute to Amazon's scientific reputation in advanced robotics
• Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots).
A day in the life
0
BASIC QUALIFICATIONS
• PhD in Computer Science, Robotics, Control, Mechanical Engineer, Electrical Engineering, or a related field with a focus on control, learning, and/or robotics.
• Deep understanding of safety-critical control, including control barrier functions and safety filters.
• Proficiency in C++ and Python with experience implementing control algorithms and/or learning policies
• Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet)
• Experience validating on physical robotic hardware (not simulation-only)
• Track record of publications at top-tier venues in control and robotics (e.g., RSS, ICRA, IROS, CDC, CoRL, NeurIPS, ICLR, L-CSS, RAL, TRO, TAC)
PREFERRED QUALIFICATIONS
• Understanding of locomotion, reduced order models, layered control architectures, nonlinear control, reachability methods, and whole-body control
• Knowledge of learning-based approaches to robotics (e.g., reinforcement learning, diffusion, VLAs, VLMs, world models.)
• Exposure to learning-based approaches for CBF synthesis (e.g., neural CBFs, data-driven barrier functions) and the integration of CBFs into learning (e.g., CBF-RL)
• Understanding of control systems engineering, with a specific focus on layered architecture used in robotic systems (high level planning, mid-level trajectory generation and low-level feedback control)
• Experience with perception on robotic systems (e.g., depth camera and LiDAR based sensing modalities, sensor fusion, semantic tagging).
• Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches
• Knowledge of safety-constrained RL (e.g., constrained MDPs, Lagrangian methods, shielding, CBF-based policy filtering)
• Experience with model-based control (MPC, whole-body QP controllers, operational space control) and/or simulation-based predictive control (MPPI)
• Experience with hierarchical RL, skill composition, distillation, and multi-task policy architectures for locomotion
• Familiarity with real-time deployment constraints (latency budgets, onboard compute limitations, control-loop frequencies)
• Experience building or contributing to large-scale RL training infrastructure (distributed training, GPU clusters)
• Strong communication skills and ability to work across disciplinary boundaries (ML, controls, mechanical engineering)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company's reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, PASADENA - 142,800.00 - 193,200.00 USD annually
We are seeking a Postdoctoral Scholar to join the SAF Lab. In this role, you will perform research around safe autonomy on highly dynamic robots, with a special focus on loco-manipulation and dynamically stable robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and robust whole-body control, layered safety filters that interface with learning modules, and the synthesis of CBFs from perception data and semantic information. You will push the boundaries of safe autonomy and validate your discoveries experimentally on the next generation of robotic platforms.
The SAF lab provides a unique opportunity to collaborate with the inventor of CBFs, top scientists and engineers at Amazon developing the next generation of safe autonomy, while also establishing strong connections with top academic research labs. Your research in the SAF lab will lay the foundations of safe learning on complex robots - removing bottlenecks to deployment and enable them to safely operate around humans.
Key job responsibilities
In this role you will:
• Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains.
• Develop the simulation and evaluation pipelines needed to run complex and large-scale validation of methods developed in high fidelity simulation environments.
• Develop sim-to-real transfer pipelines that enable the deployment of simulation-based methods (controllers, policies) on hardware.
• Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research.
• Publish research at top-tier robotics, control and ML venues and contribute to Amazon's scientific reputation in advanced robotics
• Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots).
A day in the life
0
BASIC QUALIFICATIONS
• PhD in Computer Science, Robotics, Control, Mechanical Engineer, Electrical Engineering, or a related field with a focus on control, learning, and/or robotics.
• Deep understanding of safety-critical control, including control barrier functions and safety filters.
• Proficiency in C++ and Python with experience implementing control algorithms and/or learning policies
• Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet)
• Experience validating on physical robotic hardware (not simulation-only)
• Track record of publications at top-tier venues in control and robotics (e.g., RSS, ICRA, IROS, CDC, CoRL, NeurIPS, ICLR, L-CSS, RAL, TRO, TAC)
PREFERRED QUALIFICATIONS
• Understanding of locomotion, reduced order models, layered control architectures, nonlinear control, reachability methods, and whole-body control
• Knowledge of learning-based approaches to robotics (e.g., reinforcement learning, diffusion, VLAs, VLMs, world models.)
• Exposure to learning-based approaches for CBF synthesis (e.g., neural CBFs, data-driven barrier functions) and the integration of CBFs into learning (e.g., CBF-RL)
• Understanding of control systems engineering, with a specific focus on layered architecture used in robotic systems (high level planning, mid-level trajectory generation and low-level feedback control)
• Experience with perception on robotic systems (e.g., depth camera and LiDAR based sensing modalities, sensor fusion, semantic tagging).
• Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches
• Knowledge of safety-constrained RL (e.g., constrained MDPs, Lagrangian methods, shielding, CBF-based policy filtering)
• Experience with model-based control (MPC, whole-body QP controllers, operational space control) and/or simulation-based predictive control (MPPI)
• Experience with hierarchical RL, skill composition, distillation, and multi-task policy architectures for locomotion
• Familiarity with real-time deployment constraints (latency budgets, onboard compute limitations, control-loop frequencies)
• Experience building or contributing to large-scale RL training infrastructure (distributed training, GPU clusters)
• Strong communication skills and ability to work across disciplinary boundaries (ML, controls, mechanical engineering)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company's reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, PASADENA - 142,800.00 - 193,200.00 USD annually
About Amazon
Sourced by ZipRecruiter
Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.
Industry
It services, book publishers, retail, real estate, computer and electronic product manufacturing and software development
Company size
10,000+ Employees
Headquarters location
Seattle, WA, US