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Reinforcement Learning Robotics Jobs in Seattle, WA

Team Red Dog

AI Robotics Engineer

Redmond, WA · On-site

Robotics Simulation & Machine Learning - Experience working with simulation environments or robotic hardware, including reinforcement learning, diffusion models, or robotic AI workflows. Opportunity ...

Team Red Dog

AI Robotics Engineer

Redmond, WA

Robotics Simulation & Machine Learning - Experience working with simulation environments or robotic hardware, including reinforcement learning, diffusion models, or robotic AI workflows. Opportunity ...

Meta

Research Engineer Robotics (Systems)

Redmond, WA · On-site

$183K - $257K/yr

Deploy and iterate on learned control policies (imitation learning, MPC, reinforcement learning) within full robotic systems. Partner with research teams to bridge the gap between algorithmic ...

Meta

AI Research Scientist, Robotics

Redmond, WA

$154K/yr

VLM, robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation, navigation, or ...

Meta

AI Research Scientist, Robotics

Redmond, WA

$219K/yr

Vision Language Models robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation ...

Cobot

AI Research Engineer

Seattle, WA · On-site

Develop and evaluate reinforcement learning algorithms and frameworks for autonomous behaviors ... While direct robotics manipulation experience is not required, familiarity with robotic systems or ...

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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.

What are popular job titles related to Reinforcement Learning Robotics jobs in Seattle, WA? For Reinforcement Learning Robotics jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Robotics jobs in Seattle, WA look for? The top searched job categories for Reinforcement Learning Robotics jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Reinforcement Learning Robotics jobs? Cities near Seattle, WA with the most Reinforcement Learning Robotics job openings:
Reinforcement Learning Robotics jobs near you

AI Robotics Engineer

Team Red Dog

Redmond, WA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 11 hours ago

Job description

Team Red Dog is hiring an AI Robotics Engineer for our client, a world-class innovation client and AI-driven technology organization advancing the future of embodied AI and robotic foundation models. In this hands-on engineering role, you'll develop machine learning pipelines, integrate robotic simulation environments, and deploy AI models that bridge cutting-edge research with real-world robotic systems. Working alongside leading researchers, you'll help advance open-source robotics software, improve model performance, and contribute to technologies shaping the next generation of intelligent autonomous systems. This is an exceptional opportunity for engineers with robotics, machine learning, and software development experience who want to make a measurable impact on groundbreaking AI research.
Top Required Skills (Must Haves):
  1. Python (2+ years) - Develop machine learning pipelines, automation tools, robotics software, and production-quality code supporting research initiatives.
  2. PyTorch (2+ years) - Train, fine-tune, evaluate, and optimize robotics foundation models, vision-language-action models, and other deep learning architectures.
  3. ROS2 (2+ years) - Integrate robotic perception and control systems with simulation environments and physical robotic hardware.
  4. Robotics Simulation & Machine Learning - Experience working with simulation environments or robotic hardware, including reinforcement learning, diffusion models, or robotic AI workflows.

Opportunity Overview:
Join a research team pushing the boundaries of embodied AI and robotic foundation models. This role combines software engineering, machine learning, robotics, and cloud-based model development to help create intelligent robotic systems capable of learning from both simulation and real-world environments. You'll collaborate with world-class researchers, contribute to open-source robotics platforms, and directly influence technologies that are advancing the future of robotics research.
How you will make an impact:
  • Develop, fine-tune, and maintain AI and machine learning models supporting robotics research.
  • Design and improve automation infrastructure for robotics testing and model evaluation.
  • Build and maintain training and evaluation pipelines using Azure Machine Learning.
  • Analyze, prepare, and refine training datasets for robotics and embodied AI applications.
  • Develop deployment tools that rapidly transition new algorithms from simulation to robotic hardware.
  • Integrate robotic perception and control stacks with simulation frameworks.
  • Benchmark robotics foundation models across simulation and real-world environments.
  • Troubleshoot software issues, debug production systems, resolve customer deployment challenges, and support open-source robotics software.
  • Collaborate within an agile research environment to deliver features, evaluations, documentation, and technical improvements.

The expertise you bring:
  • Bachelor's degree in Computer Science, Computer Engineering, Robotics, or a related technical discipline.
  • 2-4 years of professional or equivalent academic experience in AI, robotics, machine learning, or software engineering.
  • Minimum 2 years of professional experience with Python.
  • Minimum 2 years of professional experience using PyTorch.
  • Minimum 2 years of professional experience with ROS2.
  • Strong computer science fundamentals including algorithms, data structures, and software design.
  • Experience working with robotic simulation environments or robotic hardware.
  • Experience troubleshooting, debugging, unit testing, and supporting production software systems.
  • Experience developing machine learning models using frameworks such as PyTorch or TensorFlow.
  • Familiarity with Azure Machine Learning or similar cloud-based ML platforms is preferred.
  • Experience with reinforcement learning, diffusion models, vision-language-action models, or robotic foundation models is highly desirable.

What makes a candidate highly successful in this role:
Successful candidates combine strong software engineering fundamentals with hands-on robotics and machine learning experience. Candidates who have participated in university robotics research labs, collaborated on robotics-focused academic projects, or contributed to embodied AI research will stand out. Experience training robotic learning models, integrating robotics software with simulation or hardware, and thriving in collaborative research environments will position candidates for success.
Why Work with Team Red Dog?
At Team Red Dog, people are at the heart of everything we do. Our commitment to personalized service and our deep experience in matching talented professionals with meaningful roles at some of the world's most inspiring companies is what sets us apart. We take the time to understand your unique skills, strengths, and passions-because we believe your career should reflect who you are.
Whether you're looking to grow, pivot, or simply find a place where your work truly matters, we offer opportunities that empower you to make a positive impact. With excellent benefits, a supportive team, and a role where you can thrive while doing what you love, we're here to help you take the next step with confidence. Join us-and discover what it means to be genuinely valued in your career.
Generous benefits package for qualified employees includes:
  • Health insurance (medical, dental, vision, and life)
  • Employer-matched 401K plan
  • Paid time off
  • Paid holidays

Estimated Start Date:
Immediately
Location:
Onsite, Redmond, WA
Job #
2550
Job Type and Estimated Duration:
W2 contract opportunity through 1/7/2028 subject to performance, budget and client discretion.
Rate:
$10,200-$11,500 per month
Team Red Dog is committed to providing equal opportunities to everyone, regardless of race, ethnicity, gender, age, religion, sexual orientation, disability, or any other characteristic. If you need accommodation during the recruitment process, reach out to hr@teamreddog.com, and we will work to ensure an accessible experience. We strictly adhere to federal, state, and local laws to maintain a workplace free from discrimination and harassment.
We offer competitive compensation aligned with U.S. industry standards, and our final offer will reflect the candidate's location, job-specific skills, experience, and knowledge.
  • All applicants must be authorized to work in the U.S. without the need for sponsorship.
  • Team Red Dog is an E-Verify employer.
  • Employment is contingent upon the successful completion of a reference and background check.
  • Please no solicitations from C2C or recruiting firms.

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