Robotics Machine Learning Engineer
Persona AI- Location
- Pensacola, FL · Houston, TX
- Workplace
- —
- Employment
- Full Time
- Salary
- —
Posted 2mo ago
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]
Job Title
Robotics Machine Learning Engineer
Department
Machine Learning
Reports To
Head of Machine Learning
Employment Type
Full-Time
Location
Pensacola, FL or 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 Machine Learning Engineer to drive our machine learning strategy. We are primarily interested in candidates who have developed and released products to market, but can be flexible depending on aptitude and energy.
As one of the inaugural Machine Learning Engineers at Persona, you will have an incredible opportunity to get in at the beginning to shape the design and development of Persona’s humanoid robot.
WHAT YOU WILL BE DOING
- Collaborate on the design and development of the Persona ML software stack and support its application in manipulation, navigation, locomotion, and perception.
- 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.
- Work with the team to develop, test, and deploy software, machine learning pipelines, and data collection pipelines.
- Monitor and evaluate the performance of models in the real world.
- Collaborate with Universities and other companies.
- 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 embodied AI.
- Enthusiasm for working collaboratively in a high paced team environment.
- 3+ years of experience in machine learning applied to robotics.
- Experience with deep learning frameworks (Pytorch, JAX, TensorFlow, etc.)
- Experience with cloud computing to develop models, store data, etc. (AWS, Azure, GCP)
- Strong understanding of the state of the art research in robot learning (behavior cloning for manipulation, reinforcement learning for locomotion, world models, etc.).
- Understanding of the challenges of deploying neural network models in the real world.
- Experienced in deploying both traditional and learning based approaches for robotics.
- Capable of writing high quality software.
- Thrive in fast paced and ambiguous environments.
- Strong first principles thinker.
BONUS SKILLS
- An advanced degree (Masters or PhD) in computer science, robotics, machine learning, or another related field.
- Published papers at top ML/Robotics conferences (ICML, ICRA, CoRL, RSS, NeurIPS).
- Have deployed robots, collected large amounts of data, and trained large neural networks that work in production environments.
Persona AI is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, age, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law.
Skills
- Machine Learning
- Deep Learning
- PyTorch
- JAX
- TensorFlow
- AWS
- Azure
- GCP
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