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Reinforcement Learning Robotics Jobs in Oregon (NOW HIRING)

Agility Robotics

Staff AI Research Engineer

Salem, OR · On-site

$216K - $338K/yr

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Agility Robotics

Staff AI Research Engineer

Salem, OR · On-site +1

$216K - $338K/yr

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Agility Robotics

Senior AI Research Engineer

Salem, OR · On-site +1

$195K - $304K/yr

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Agility Robotics

Senior AI Research Engineer

Salem, OR · On-site

$195K - $304K/yr

Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to ...

Agility Robotics

Senior Manager, AI Innovation

Salem, OR · On-site +1

$268K - $364K/yr

Strong expertise in core AI domains, including computer vision, reinforcement learning, and large language models (LLMs) applied to robotics. * Proven track record of managing scaling high ...

All JobsReinforcement Learning Robotics JobsReinforcement Learning Robotics Jobs in Oregon

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?

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.

What are popular job titles related to Reinforcement Learning Robotics jobs in Oregon?

For Reinforcement Learning Robotics jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Robotics jobs in Oregon look for?

The top searched job categories for Reinforcement Learning Robotics jobs in Oregon are:

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Cities in Oregon with the most Reinforcement Learning Robotics job openings:

Reinforcement Learning Robotics jobs near you

Senior AI Software Engineer, Reinforcement Learning

Agility Robotics

Salem, OR • On-site

$187K - $292K/yr

Full-time

Posted 24 days ago

Job description

Agility's commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.

Agility is a pioneer. Our robot, Digit, is the first to be sold into workplaces across the globe. Our team is differentiated by its expertise in imagining, engineering, and delivering robots with advanced mobility, dexterity, intelligence, and efficiency -- robots specifically designed to work alongside people, in spaces built for people. Every day, we break through engineering challenges and invent new solutions and capabilities that will one day make robots commonplace and approachable. This work is our passion and our responsibility: our mission is to make businesses more productive and people's lives more fulfilling.

Why Join Now

Digit is one of the only humanoid robots deployed in real customer facilities today—at Schaeffler, GXO, Toyota Motor Manufacturing Canada, and Mercado Libre, with 65,000+ hours of real-world operation logged. In June 2026 we announced plans to become the first pure-play humanoid robotics company to trade publicly (ticker AGLT), in a ~$2.5B merger backed by NVIDIA, Amazon, SoftBank Vision Fund 2, and Foxconn. With $300M+ in orders for Digit and a factory built for 10,000 robots a year, we're scaling fast—and the policies you build will ship to robots working alongside people in production, not sit in simulation.

About the Role

The AI Controls team builds high-rate learned controllers that let Digit move robustly, efficiently, and safely in dynamic environments. As an AI Controls Engineer, you'll develop and deploy reinforcement learning policies across humanoid locomotion, whole-body control, and manipulation—integrating perception to enable collision-free, perceptive motion in the real world.

About The Work

  • Design, train, and deploy robust RL policies for locomotion, manipulation, whole body control, and dynamic interactions with the environment.
  • Integrate perception into RL policies to achieve obstacle-aware, collision-free motion, and perceptive manipulation.
  • Develop and maintain core RL infrastructure, including scalable training pipelines and evaluation frameworks.
  • Design and implement new simulation environments and tasks to support training and evaluation of control policies.
  • Collaborate with on-robot software and deployment teams to ship production-quality policies to Digit.

About You

  • 4+ years of experience developing and deploying RL policies for robotics applications.
  • Strong Python skills and hands-on experience with a deep learning framework such as PyTorch.
  • Experience designing reward functions, tuning hyperparameters, and implementing exploration strategies to solve complex control tasks.
  • Experience with perception-in-the-loop control, integrating real-time sensory inputs for reactive or adaptive behaviors.
  • Proven experience deploying reinforcement learning policies on real-world bipedal or quadrupedal robots.
  • Familiarity with robot simulation environments (e.g. Mujoco-Warp, Isaac) and sim-to-real transfer.
  • A collaborative approach and the ability to deliver safe, high-quality software in a fast-paced environment.

Bonus Qualifications

  • Advanced degree (MS or PhD) in Robotics, Computer Science, or a related field.
  • Experience with contact-rich manipulation, including force-torque or tactile sensing.
  • Familiarity with policy distillation (e.g. teacher–student) for transferring state-based policies to perception-driven ones.
  • Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA).

This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.

Anticipated Salary Range
$187,000—$292,000 USD

In addition to base pay, our competitive total rewards package consists of the following for full-time employees:

  • 401(k) Plan: Includes a 6% company match.
  • Equity: Company stock options.
  • Insurance Coverage: 100% company-paid medical, dental, vision, and short/long-term disability insurance for employees.
  • Benefit Start Date: Eligible for benefits on your first day of employment.
  • Well-Being Support: Employee Assistance Program (EAP).
  • Time Off:
    • Exempt Employees: Flexible, unlimited PTO and 12 company holidays, including a winter shutdown.
    • Non-Exempt Employees: 10 vacation days, paid sick leave, and 12 company holidays, including a winter shutdown, annually.
  • On-Site Perks: Catered lunches four times a week and a variety of healthy snacks and refreshments at our Salem and Pittsburgh locations.
  • Parental Leave: Generous paid parental leave programs.
  • Work Environment: A culture that supports flexible work arrangements.
  • Growth Opportunities: Professional development and tuition reimbursement programs.
  • Relocation Assistance: Provided for eligible roles.
  • Annual Discretionary Bonus: Provided for eligible roles.

All of our roles are U.S.-based. Applicants must have current authorization to work in the United States.

Agility Robotics is committed to a work environment in which all individuals are treated with respect and dignity. Each individual has the right to work in a professional atmosphere that promotes equal employment opportunities and prohibits unlawful discriminatory practices, including harassment. Therefore, it is the policy of Agility Robotics to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Agility Robotics prohibits any such discrimination or harassment.

Agility Robotics does not accept unsolicited referrals from third-party recruiting agencies. We prioritize direct applicants and encourage all qualified candidates to apply directly through our careers page. If you are represented by a third party, your application may not be considered. To ensure full consideration, please apply directly.