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Reinforcement Learning Robotics Jobs in Austin, TX

Nvidia

Senior Compiler Engineer - AI

Austin, TX · On-site

$121K - $160K/yr

An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ... The ideal candidate brings broad experience across machine learning, including reinforcement ...

Nvidia Corporation

Senior Compiler Engineer - AI

Austin, TX · On-site

$121K - $160K/yr

An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ... The ideal candidate brings broad experience across machine learning, including reinforcement ...

Nscale

VP, Physical AI GTM

Austin, TX

$370K - $650K/yr

... robotics, autonomous cars, uncrewed aerial vehicles, and smart manufacturing-is a massive, specialized growth vector that requires unparalleled compute power for simulation, reinforcement learning ...

Nscale

VP, Physical AI GTM

Austin, TX · On-site

$370 - $650/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... robotics, autonomous cars, uncrewed aerial vehicles, and smart manufacturing--is a massive, specialized growth vector that requires unparalleled compute power for simulation, reinforcement learning ...

Socket.dev

VP, Physical AI GTM

Austin, TX · On-site

$370 - $650/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... robotics, autonomous cars, uncrewed aerial vehicles, and smart manufacturing--is a massive, specialized growth vector that requires unparalleled compute power for simulation, reinforcement learning ...

New

Nscale

VP, Physical AI GTM

Austin, TX · On-site

$370K - $650K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... robotics, autonomous cars, uncrewed aerial vehicles, and smart manufacturing-is a massive, specialized growth vector that requires unparalleled compute power for simulation, reinforcement learning ...

AlertMedia

Sales Enablement Manager

Austin, TX · Remote

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Our core values drive us in our important mission of keeping people safe: * We're humans not robots ... Create and maintain training and learning content, including presentation slides, one-pagers, job ...

AlertMedia

Sales Enablement Manager

Austin, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Our core values drive us in our important mission of keeping people safe: * We're humans not robots ... Create and maintain training and learning content, including presentation slides, one-pagers, job ...

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All JobsReinforcement Learning Robotics JobsReinforcement Learning Robotics Jobs in Austin, TX

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 Austin, TX?

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

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The top searched job categories for Reinforcement Learning Robotics jobs in Austin, TX are:

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Cities near Austin, TX with the most Reinforcement Learning Robotics job openings:

Reinforcement Learning Robotics jobs near you

Lead Software Engineer - Dexterous Manipulation

Apptronik

Austin, TX • On-site

Full-time

Re-posted 7 days ago

Job description

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.
We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.
JOB SUMMARY
The Lead Software Engineer - Dexterous Manipulation is a core contributor to our robot's ability to interact with the world with human-like precision. This role is responsible for leading the development of learning-based dexterous control algorithms that unlock the full potential of state-of-the-art robotic hand hardware.
This role will bridge the gap between cutting-edge research and scalable, reliable production software. Whether leveraging reinforcement learning, imitation learning, teleoperation retargeting, or classical control, they will ensure our robots can perform complex, high-DOF tasks in both simulation and reality. As a technical lead, they will not only design the core software architecture but also influence hardware design to achieve world-class manipulation capabilities.
ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES
  • Strategic Ownership: Serve as the technical authority for dexterous manipulation. Create the long-term technical roadmap, ensuring hand control and multi-fingered coordination capabilities outpace industry standards.
  • Architectural Definition: Design and enforce the foundational software frameworks for manipulation. Own the decision-making process for balancing autonomous logic with high-fidelity teleoperation, ensuring the architecture is scalable for future hardware generations.
  • Research & Innovation: Perform and direct the integration of state-of-the-art research. Select and deploy the specific learning-based policies and vision-integrated systems that will define system physical capabilities.
  • Sim-to-Real Ownership: Lead the strategy for high-fidelity simulation. Set the standards for success in virtual environments to optimize policy transitions to physical fleet hardware.
  • Hardware Design Influence: Drive the specifications for next-generation hardware. Define the requirements for sensing, degrees of freedom, and torque profiles.
  • Production Excellence: Oversee the transition from experimental research to fleet-wide deployment. Ensure performance and reliability of C++/Python code running on production-level assets.
  • Technical Leadership & Culture: Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders.

SKILLS AND REQUIREMENTS
Technical Skills (Must-Have)
  • Dexterous Manipulation: Deep expertise in multi-fingered hand control, grasp planning, and in-hand manipulation, with a strong track record of successful hardware deployment.
  • Advanced Control & Learning: Proficiency in learning-based control for robotics (e.g. flow/diffusion-based visiomotor policies, reinforcement learning, reward modeling, etc.).
  • Software Engineering: Proficiency in Python, with experience building real-time robotic software stacks.
  • Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation.

Good to Have
  • Robotic Kinematics: Strong foundation in spatial transformations, Jacobian-based control, and constrained optimization.
  • Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control.
  • Tactile Sensing: Experience integrating tactile/haptic feedback into manipulation pipelines.
  • Computer Vision: Familiarity with 6D pose estimation, point cloud processing, or visual-servoing.
  • Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic end-effectors.

EDUCATION and/or EXPERIENCE
  • BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
  • 5+ years of relevant experience (or 3+ years with a PhD) specifically focused on robotic manipulation or complex motion control.
  • A proven track record of taking complex algorithms from a research/simulation environment and successfully deploying them on physical hardware.

PHYSICAL REQUIREMENTS
  • Prolonged periods of sitting at a desk and working on a computer
  • Must be able to lift 15 pounds at times
  • Vision to read printed materials and a computer screen
  • Hearing and speech to communicate

*This is a direct hire. Please, no outside Agency solicitations.
Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

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