Robot Learning Intern

About the role

This role develops learning methods that improve robot dexterity and builds research algorithms for real robot manipulation and navigation. The intern joins a team creating a unified robotic platform that makes building and deploying physical AI as accessible as software development.

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

New algorithms and methods improve robot dexterity, and the resulting models train robots to perform tasks more reliably.

Cutting-edge research spans Robotics, RL/IL, control, perception, and related fields to advance physical AI methods.

State-of-the-art learning-based manipulation, navigation, and control algorithms are designed and implemented on real robots to test behavior in practical settings.

Robust manipulation skills for robots, such as VLA and WAM, are developed together with other teams to broaden what robots can do.

Requirements

Current enrollment in a PhD program or a master degree in a relevant technical field is required.

A strong passion for working with robots guides the intern's contributions to research and system experiments.

Research experience in embodied AI, robotics, computer vision, machine learning, human-AI interaction, or computer science demonstrates readiness for the role.

Experience with deep learning frameworks such as PyTorch is necessary to implement and train learning-based robot algorithms.

Solid understanding of SOTA robot learning techniques, including reinforcement learning and imitation learning, is required.

Experience with robot simulators such as Isaac Gym, Isaac Sim, SAPIEN, MuJoCo, Drake, or similar platforms is required.

Experience building systems based on machine learning and/or deep learning methods is mandatory for deployment and experimentation.

Practical notes

The role is based in the Singapore Office and is full_time.

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

Robot learning research focuses on teaching robots to adapt behaviors from data and interaction.

Reinforcement learning and imitation learning are core methods for training control policies in embodied systems.

Physical AI platforms combine hardware, software, and learning methods to lower entry barriers for builders.

Research contributions often appear in top conferences and journals that highlight advances in robotics and machine learning.

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.