Job Title: Senior Robotics Software Engineer, Autonomy

Department: Software Engineering

Employment Type: Full-Time

FLSA Status: Exempt

Location: Houston, TX

Travel: 10%

Who We Are

Persona AI is building humanoid robots for the most demanding environments in heavy industry — shipyards, steel mills, fabrication facilities, and offshore platforms — performing welding, grinding, maintenance, inspection, and material-handling work that is dangerous, physically demanding, and increasingly difficult to staff.

We are backed by leading strategic and financial investors and engaged with global industrial leaders across Korea, Japan, the United States, and Singapore. Korea is the center of gravity for our early commercial strategy, anchored by relationships with the world’s leading shipbuilders and steelmakers. Our work spans both the robot platform itself and the systems, partners, and playbooks required to deploy it at scale.

Why Join Persona AI?

We offer competitive compensation, a performance-based bonus, 100% employer covered medical benefits for most healthcare plans, early-stage equity, competitive PTO, and company-wide paid winter break between December 24th and January 2nd.

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

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

About the Role

Persona AI is building a rugged humanoid platform to supplement the industrial skilled-labor workforce. Our robots are designed to perform complex tasks such as welding, grinding, and painting in demanding real-world environments.

To succeed, they must understand the job site, translate high-level objectives into executable plans, carry out those plans safely and reactively, and adapt as conditions change. We are looking for a Senior Robotics Software Engineer, Autonomy to help design and build the autonomy stack that enables these capabilities, from simulation through deployment on the shop floor.

This role combines world modeling with proven autonomy techniques to develop intelligent behaviors for humanoids operating at live industrial sites. We train the robot’s world model on representative industrial tasks and environments, giving it a deep understanding of the work it must perform. Before each new deployment, we then refine and validate its behavior in a digital twin simulation of the customer’s facility to ensure safety, reliability, and performance.

What You Will Be Doing

Design and implement autonomy and behavior coordination algorithms for an industrial humanoid platform

Work with manipulation, navigation, perception, and teleop engineers to integrate robot skills into autonomous robot behaviors

Develop plan execution approaches that combine classical control and learned policies

Design and implement behavior execution that is reactive, safe, adaptable, and explainable

Integrate autonomy with robot safety, health monitoring, bring-up, and safe shutdown

Design and implement pipelines for autonomous behavior evaluation, benchmarking, and regression testing

Implement data curation pipelines that enable our robots to understand a variety of real-world industrial tasks

Develop synthetic data generation pipelines for post-training of humanoid world models

What We Are Looking For

MS or PhD in Robotics, Computer Science, or a related field

5+ years of professional experience in robotics, with a primary focus on autonomy

Experience with plan execution approaches such as behavior trees and state machines

Strong hands-on experience with NVIDIA IsaacSim and Mujoco for robotic simulation

Strong fundamentals in Python, C++, data structures and algorithms, git, code review, CI/CD

Experience with synthetic data generation, data curation, and benchmark evaluation

Bonus Skills

Exposure to humanoid robot locomotion, navigation, manipulation, or perception

Experience with NVIDIA Isaac Lab for reinforcement learning environment setup and training

Experience with ROS2 libraries for autonomous planning and navigation

Experience with generative and symbolic planning approaches