Research scientist (Robotics, AI)

About the role

This role advances physical AI by creating methods that teach robots to manipulate and navigate. The work bridges algorithmic research and large-scale model training on real robot systems.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Algorithms are developed to improve robot dexterity through training methods for AI models. Research spans multiple disciplines, including robotics, reinforcement and imitation learning, control, perception, LLMs, and VLMs. Diverse manipulation skills are built through collaboration with other teams to strengthen robot capabilities.

Requirements

A Ph.D. is required in Robotics, Computer Science/Engineering, Electrical Engineering, Mechanical Engineering, or equivalent research experience. Candidates must have at least 3 years of experience conducting independent research. Deep understanding of state-of-the-art robot learning techniques, including reinforcement learning and imitation learning, is required. A record of research excellence is demonstrated through publications in top conferences and journals such as Science Robotics, IJRR, RSS, CoRL, ICRA, NeurIPS, ICML, ICLR, CVPR, and more. Python proficiency is required for daily work. Proficiency with deep learning libraries such as PyTorch, TensorFlow, or Jax is required. Experience with real robot experiments is required. Experience with robot simulators such as Isaac Gym, Isaac Sim, SAPIEN, MuJoCo, or Drake is required.

Practical notes

Work is conducted from the Fremont Office. Collaboration with multiple teams is required. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Physical AI research focuses on teaching robots manipulation and navigation through algorithms. The role uses large-scale model training and simulation tools to bridge research and real-world systems. Success depends on publishing in top-tier conferences and journals. Python and deep learning frameworks are central to daily tasks. Real robot experiments and simulator workflows are core to validating research results.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

We are an early-stage robotics startup working on building multi-purpose mobile robots that can do complex manipulation tasks. We are looking for a creative, skilled, and motivated research scientists to join our founding team in advancing robot manipulation capabilities.