EngineeringNot provided by source

Robotics Software Internship, Manipulation

Persona AI Inc · Not provided by source

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Last seen by MeritLog September 12, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from Persona AI Inc's Ashby job board and last checked it on September 12, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model: Not provided by source
Salary: Not listed by source
Location: Houston, TX
Company website: persona.ai

Hiring context

How this role compares at Persona AI Inc

Persona AI Inc has 29 live roles in MeritLog’s catalog across 5 job families, and 20 of them are in engineering. 0 of those listings publish a pay range, a disclosure rate of 0%.

Persona AI Inc concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What they're asking for

Currently pursuing a BS, MS, or PhD in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field.Education
Coursework, lab research, or project experience (capstone projects, robotics competitions, personal projects) involving control and manipulation of robotic or humanoid arms.Skill
Foundational knowledge of motion planning, control theory, and optimization.Skill
Programming proficiency in C++ and/or Python.Skill
Exposure to relevant robotics libraries or tools (e.g., ROS, MoveIt!, Drake, OpenRAVE) through coursework or projects.Skill
Familiarity with deep learning fundamentals as applied to robotics or manipulation.Skill
Some exposure to computer vision concepts, such as sensors, point clouds, segmentation, and object detection, through coursework or projects.Skill
A strong desire to learn quickly and contribute in a fast-paced startup environment.Skill
Project or research experience with contact modeling, force/torque estimation, or tactile sensing for dexterous tasks.SkillPreferred
Exposure to machine learning for dexterous manipulation, such as learning-based grasp strategies, behavior cloning, or reinforcement learning.SkillPreferred
Published research related to manipulation.SkillPreferred
Prior internship or hands-on experience in a robotics, autonomy, or hardware startup environment.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

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Job Title: Robotics Software Internship, Manipulation Department: Software Engineering Reports To: Manipulation Lead Employment Type: Internship Location: Houston, Texas – Required (on-site) 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. Your Role: - Help design and implement dexterous manipulation algorithms for humanoid robots with high-DOF, multi-fingered hands, working closely with senior engineers. - Contribute to the manipulation pipeline (perception, grasping, trajectory optimization, motion planning, and control) on tasks like precise grasping, dual-arm manipulation, and in-hand object manipulation. - Support the implementation and testing of control strategies for dexterous manipulation, including force/motion control and visual/tactile servoing. - Assist with integrating manipulation components into the humanoid robot's whole-body controller and loco-manipulation efforts. - Build and iterate on digital-twin simulation environments for algorithm development using simulators such as MuJoCo and Isaac Sim. - Run and help design tests in both simulated and real-world environments, and help analyze the results. - Partner with the machine learning team to help train and deploy models for advanced manipulation tasks. - Stay current on manipulation research and bring relevant ideas, traditional and deep learning-based, back to the team. We're Looking For: - Currently pursuing a BS, MS, or PhD in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field. - Coursework, lab research, or project experience (capstone projects, robotics competitions, personal projects) involving control and manipulation of robotic or humanoid arms. - Foundational knowledge of motion planning, control theory, and optimization. - Programming proficiency in C++ and/or Python. - Exposure to relevant robotics libraries or tools (e.g., ROS, MoveIt!, Drake, OpenRAVE) through coursework or projects. - Familiarity with deep learning fundamentals as applied to robotics or manipulation. - Some exposure to computer vision concepts, such as sensors, point clouds, segmentation, and object detection, through coursework or projects. - A strong desire to learn quickly and contribute in a fast-paced startup environment. Preferred or Bonus Qualifications: - Project or research experience with contact modeling, force/torque estimation, or tactile sensing for dexterous tasks. - Exposure to machine learning for dexterous manipulation, such as learning-based grasp strategies, behavior cloning, or reinforcement learning. - Published research related to manipulation. - Prior internship or hands-on experience in a robotics, autonomy, or hardware startup environment. Our Internship Program At Persona AI, our internship program gives you direct, hands-on experience building cutting-edge humanoid robotics alongside industry experts. We offer full-time term opportunities across three upcoming cohorts: - Fall 2026: August – December - Spring 2027: January – May - Summer 2027: May – August As an intern, you’ll be paired 1:1 with a senior engineer mentor to accelerate your growth and guide your technical contributions. You will bond with your cohort through intern group lunches, gain cross-functional perspective during company-wide learning lunches, and unwind with fun team activities throughout the term. Whether you're working on advanced motion planning or real-world system testing, you'll tackle meaningful engineering challenges while immersing yourself in our collaborative startup culture. Application Review Process We evaluate applications as they are received rather than waiting for the posted deadline to pass. Positions for each cohort are filled on a first-come, first-served basis, and the portal will close as soon as all available roles are finalized. To give yourself the best chance of securing a spot, keep these tips in mind: - Apply Early: Submitting your resume as soon as possible ensures your materials are reviewed before interview slots fill up. - Cohort Availability: Popular terms-especially Summer 2027-tend to reach capacity quickly. - Rolling Interviews: Interviews and candidate evaluations begin immediately upon application receipt. 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.

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