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Reinforcement Learning Robotics Jobs in Freehold, NJ

Amazon

Robotics/AI Motor Control Scientist, Fauna

New York, NY · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning ... Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation ...

Meta

AI Research Scientist, Physical AI

New York, NY · On-site

$184K - $257K/yr

... reinforcement learning, representation learning, self-supervised learning, multimodal learning, robotics policy development, computer vision (3D), egocentric perception, embodied AI and/or LLMs ...

New

Meta

AI Research Scientist, Physical AI

New York, NY

$184K/yr

Research experience in at least one of the following research areas: physical AI, simulation, reinforcement learning, representation learning, self-supervised learning, multimodal learning, robotics ...

New

Amazon

Robotics/AI Motor Control Scientist, Fauna

New York, NY · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning ... Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation ...

Amazon

Robotics/AI Motor Control Scientist, Fauna

New York, NY · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning ... Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation ...

New York University

RESEARCH SCHOLAR

New York, NY · On-site

$27/hr

Hao Su ( at NYU Tandon School of Engineering is seeking to hire a Part Time Research Scholar (Non-PhD) to work on mechatronics design, control, and reinforcement learning for wearable robots ...

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Reinforcement Learning Robotics information

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 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 is the difference between Reinforcement Learning Robotics vs Machine Learning Engineer?

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData 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.

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Reinforcement Learning Robotics jobs near you

Research Scientist, Reinforcement Learning

Basis Research

New York, NY • On-site

$120K - $180K/yr

Full-time

Re-posted 25 days ago

Job description

About Basis
Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society's ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we're building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
About the Role
Research scientists lead Basis' efforts to develop a deeper understanding of the conceptual, mathematical, and computational principles of intelligence.
We are looking for people who are technically excellent, and who value probing concepts at their foundations. Our research scientists/engineers aspire to do rigorous, high-quality, robust science, but are not afraid to tinker, make mistakes, and explore radically different ideas in order to get there.
Basis is a collaborative effort, both internally and with our external partners; we are looking for people who enjoy working with others on problems larger than ones they can tackle alone.
Research Focus
Our research within the MARA project aims to develop new foundations and technologies for modeling, abstraction, and reasoning in AI systems. MARA's overarching goal is to uncover principled methods for how intelligence constructs, refines, and utilizes world models through interactive experimentation.
For this role, we are specifically looking for experts in Reinforcement Learning & Planning who can advance the state of the art in model-based RL, exploration strategies, optimal control, and Bayesian optimization. You will work on developing agents that can learn efficient policies in complex, partially observable environments by leveraging structured world models.
The immediate mission of MARA is to solve concrete challenges such as AutumnBench, physical and simulated robotics benchmarks, and the Abstract Reasoning Corpus (ARC), with the broader mission of building systems capable of learning in an open, growing portfolio of domains using human-comparable amounts of data and interaction.
Who we're looking for
  • Researchers holding a PhD in computer science, artificial intelligence, machine learning, cognitive science, or related fields.
  • Strong background in reinforcement learning, planning, MDPs, optimal control, and sequential decision making.
  • Experience in developing AI systems that combine neural and symbolic methods is highly valued.
  • Interest in foundational AI research and its applications to modeling, abstraction, and reasoning.
  • Individuals with a demonstrated track record in scientific research, evidenced through publications, technical reports, or impactful software projects.
  • Excited about solving real world problems and having positive societal impact.

Responsibilities
  • Conduct independent and collaborative research focused on the MARA project.
  • Develop new methods and algorithms for reinforcement learning, planning, and decision-making in AI systems.
  • Apply these methods to concrete challenges such as AutumnBench, physical and simulated robotics environments, and other domains.
  • Disseminate research findings through academic publications and presentations at leading conferences.
  • Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.
  • Develop and maintain open-source software
  • (Optionally) Publish and present findings in journals and conferences
  • Contribute to the culture and direction of Basis
Role Details
Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.
  • FT/PT: This is a full-time position
  • In-person Policy: We are in the office four days a week. Be prepared to attend multi-day Basis-wide in-person events.
  • Location: This role is in-person in either New York City or Cambridge, MA.
  • Salary range: Competitive salary.
  • Start date: Immediate start possible.

Non-Discrimination Notice
Basis Research Institute provides equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or genetics and prohibits discrimination based on all protected characteristics.
Privacy Notice
By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.
Read our full Global Data Privacy Notice here.

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