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Humanoid Locomotion Reinforcement Learning Engineer

Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. Persona’s founding team has a decades-long history in humanoid robotics, bionics, and product development delivering robust hardware that has touched the stars, worked miles below the surface of the ocean, roamed Disney Parks, and has even been featured on a US postage stamp. Our mission is focused squarely on shipping beautiful, reliable products at massive scale, while building a customer-focused team to achieve these aims.

We're looking for exceptional engineers who dream big, thrive on challenges, and love seeing their designs come to life. We are primarily interested in candidates who have been hands-on in the development of products released to market, in the fields of autonomy, robotics, consumer products, medical devices, automotive or aerospace. We also value advanced degrees and deep expertise.

As one of the inaugural Engineers at Persona, you will have an incredible opportunity to shape the design of Persona’s humanoid robots, while also helping set the culture of the company as it grows. If you're passionate about cutting-edge technology and want to be part of a world-class team that's driving the future of manufacturing and humanoids, we'd love to hear from you.

Your Role:

  • Develop and implement reinforcement learning training pipelines for bipedal walking. Specifically, focus on sim to real transfer, robust locomotion, and loco-manipulation.

  • Simulate, test and validate control policies in both simulation and real-world environments.

  • Collaborate with the robotics control and software teams to integrate policies within a real-time control framework.

  • Collaborate on the design and development of the Persona ML software stack to support its application in locomotion.

  • Collaborate with hardware, perception and planning teams to refine system performance and robustness.

  • Work with the ML team to craft and execute on a comprehensive plan for the development of machine learning models, keeping up to date with the state of the art in research and development.

We’re Looking For:

  • Thrive in fast paced and ambiguous environments.

  • 3+ years of experience in reinforcement learning applied to bipedal locomotion.

  • MS or PhD in Robotics, Mechanical Engineering, Computer Science, or a related field. 

  • Experience with relevant tools and libraries (Pytorch, MuJoCo, IsaacLab, etc.)

  • Proficiency in Python and capable of writing high quality software.

  • Understanding of the challenges of deploying neural network models in the real world.

  • Hands-on experience deploying control policies on real humanoid robots in real world settings.

Preferred or Bonus Qualifications:

  • Proficiency in either Rust, C, or C++ for low latency control implementation.

  • Background in kinematics, dynamics, and optimization for robotic motion.

  • Expertise in model-based control techniques (e.g., inverse dynamics, whole-body control, MPC). 

  • Published papers at top ML/Robotics conferences (ICML, ICRA, CoRL, RSS, NeurIPS).

  • Have deployed robots, collected large amounts of data, and trained neural networks that work in production environments.

  • Experience with cloud computing to develop models, store data, etc. (AWS, Azure, GCP)

Why join Persona AI?

  • You'll shape technology that's redefining the possibilities of robotics and human interaction.

  • Work alongside passionate teammates who value diversity, creativity, and continuous learning.

  • Enjoy full access to advanced prototyping tools, labs, and the freedom to experiment and innovate.

  • We offer competitive compensation, excellent benefits, flexible work environment, and equity opportunities.

Persona AI embraces diversity and equal opportunity in a serious way. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. We believe the more inclusive we are, the better our work will be.

Average salary estimate

$115000 / YEARLY (est.)
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$100000K
$130000K

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Full-time, on-site
DATE POSTED
April 15, 2025

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