Deep Learning - Robot Manipulation Engineer
Generate a McCoy IQ challenge in 30 seconds.
See how candidates think and approach the work this role demands, before the phone screen. We'll build a video challenge from this posting, and you can edit or share it before it goes live.
Key details
Job Description
[data:image/png;base64,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]
Job Title: Deep Learning - Robot Manipulation Engineer
Department: Machine Learning
Reports To: Head of Machine Learning
Employment Type: Full-Time
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, 99% employer covered medical benefits, early-stage equity, competitive PTO, and a 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
We're looking for a Deep Learning Manipulation Engineer to help train Persona AI robots to do real work in real world environments. We're looking for exceptional people who dream big, thrive on challenges, and love seeing their efforts come to life. We are primarily interested in candidates who have developed and released products to the market, but can be flexible depending on aptitude and energy.
As one of the inaugural Deep Learning Manipulation Engineers at Persona, you will have an incredible opportunity to get in at the beginning to shape the design and development of Persona’s deep learning and manipulation strategy and infrastructure. If you're passionate about cutting-edge technology and want to be part of a world-class team we'd love to hear from you.
WHAT YOU WILL BE DOING
- Design and implement advanced deep learning models and training procedures to achieve dexterous manipulation for humanoid robots with high DOF and multi-fingered hands.
- Train models, using curriculum learning strategies, to progress from simple object interactions to precise grasping, long-horizon tasks, tool-use, and in-hand object manipulation.
- Incorporate tactile sensing and proprioception into end-to-end learning pipelines, enabling robust closed-loop policies.
- Work with the teleoperation and data team to design data collection and versioning strategies.
- Leverage existing state-of-the-art manipulation models and contribute to the development of new architectures for emerging complex tasks.
- Deploy trained manipulation models to robotic hardware, ensuring real-time performance, safety, and integration with control systems and sensors.
- Collaboratively develop and optimize the manipulation ML pipeline.
- Keep up to date with the state of the art in research and development.
- Develop and execute evaluation pipelines to rigorously test learned manipulation models, including real-world trials and simulation, measuring performance, robustness, and generalization across tasks and environments.
- Collaborate in attracting, nurturing and growing the machine learning and autonomy teams.
WHAT WE ARE LOOKING FOR:
- Courage and grit to tackle some of the hardest problems in robot manipulation.
- Enthusiasm for working collaboratively in fast paced ambiguous environments.
- Masters or PhD in Robotics, Comp
Audit details(provenance, verification trail, raw fields)
Core fields
persona-ai:5211fd75-846a-488d-8f93-c4cf9c2d1e8dProvenance
persona.aiVerification trail
This posting hasn't been probed by our closure verifier yet. Stream C runs on a rolling schedule against postings approaching the close-decision threshold.
See how we measure for definitions, or our corrections log for known issues. Found something wrong? Flag a correction.
