Listing unavailableData & AnalyticsOn-site

Machine Learning Internship, Manipulation

This listing is no longer verified as available.

MeritLog keeps this source-backed description for reference. Availability is not verified, and there is no application link here.

Last seen by MeritLog September 8, 2026Source: AshbySource version: ashby-public-job-posting-v1

Source: the employer's Ashby job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model: On-site
Salary: Not listed by source
Location: Houston, TX

What the role asks for

What you'd do

Work with the ML team to develop and improve machine learning models and the infrastructure.
Monitor and evaluate the performance of models in the real world.
Collaborate on the design and development of the Persona ML software stack and support its application in manipulation, navigation, locomotion, and perception.
Courage and grit to tackle some of the hardest problems in embodied AI.
Enthusiasm for working collaboratively in a high paced team environment.
Experience with deep learning frameworks (Pytorch, JAX, TensorFlow, etc.)
Experience with cloud computing to develop models, manage data, etc. (AWS, Azure, GCP)
Strong understanding of the state of the art research in robot learning (behavior cloning and reinforcement learning for manipulation, world models, etc.).
Understanding of the challenges of deploying neural network models in the real world.
Capable of writing high-quality software.
Thrive in fast-paced and ambiguous environments.
Strong first principles thinker.

What they're asking for

An advanced degree (Master's or PhD) in computer science, robotics, machine learning, or another related field.EducationPreferred
Published papers at top ML/Robotics conferences (ICML, ICRA, CoRL, RSS, NeurIPS).SkillPreferred

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

Job description

[data:image/png;base64,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] Job Title: Machine Learning Internship, Manipulation Department: Robotics Software Engineering Employment Type: Internship Location: Houston, TX – Onsite WHO WE ARE Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. Persona AI’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, and even roamed Disney Parks. Our mission is focused squarely on shipping beautiful, reliable products at massive scale, while building a customer-focused team to achieve these aims. ABOUT THE ROLE We're looking for a Machine Learning Intern (Manipulation) to help build Persona's machine learning models and infrastructure. We are primarily interested in candidates with experience and relevant projects in robot learning, but can be flexible depending on aptitude and energy. As a Machine Learning Intern at Persona, you will have an incredible opportunity to work with humanoid robots and solve practical problems in the real world. WHAT YOU'LL DO - Work with the ML team to develop and improve machine learning models and the infrastructure. - Monitor and evaluate the performance of models in the real world. - Collaborate on the design and development of the Persona ML software stack and support its application in manipulation, navigation, locomotion, and perception. WHAT WE'RE 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. - Experience with deep learning frameworks (Pytorch, JAX, TensorFlow, etc.) - Experience with cloud computing to develop models, manage data, etc. (AWS, Azure, GCP) - Strong understanding of the state of the art research in robot learning (behavior cloning and reinforcement learning for manipulation, world models, etc.). - Understanding of the challenges of deploying neural network models in the real world. - Capable of writing high-quality software. - Thrive in fast-paced and ambiguous environments. - Strong first principles thinker. PREFERRED OR BONUS QUALIFICATIONS - An advanced degree (Master's or PhD) in computer science, robotics, machine learning, or another related field. - Published papers at top ML/Robotics conferences (ICML, ICRA, CoRL, RSS, NeurIPS). 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.

Keep exploring

Available Data & Analytics roles

These current listings are available to explore now.

Search all jobs