Agentic Robotics Research Scientist

At Sharpa, we manufacture time by making robots useful.

Founded in 2024, Sharpa is an AI robotics company inspired by the Sherpa people of the Himalayas - known for guiding and assisting alpinists on their difficult journey to the summit. We build general purpose robots that assist, not replace, humans - freeing people from repetitive and strenuous work so they can pursue inspiring endeavors.

Our team is made up of geeks and innovators with backgrounds at leading AI and robotics companies. We are dedicated to developing ultra-high performance, autonomous robotic systems capable of adapting to the complexity of real world environments. Sharpa operates out of Singapore, Shanghai and Mountain View.

What you'll be doing:

• Conduct research on agentic robot learning systems that enable robots to reason, plan, learn, and complete long-horizon tasks in real-world environments.
• Develop methods spanning foundation models, vision-language-action models, task and motion planning, reinforcement learning, imitation learning, world models, memory, and tool use.
• Explore how autonomous agents can perceive their environment, decompose goals, make decisions, recover from failure, and improve through interaction.
• Work closely with robotics, computer vision, control, and platform teams to deploy research prototypes on physical robot systems.
• Design and run experiments in simulation and on real hardware; analyse results and iterate rapidly on system performance and reliability.
• Contribute to the team’s technical research direction, including identifying promising research questions and turning them into practical capabilities.
• Publish research outcomes in leading conferences and journals, and contribute to patents or open-source work where appropriate.

What we're looking for:

• PhD in Robotics, Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Strong 1st-author publication record at recognised top-tier conferences or journals, such as NeurIPS, ICML, ICLR, CoRL, RSS, TRO, IJRR, ICRA, IROS, CVPR.
• Strong understanding of modern AI and robotics methods, with experience in one or more areas including embodied AI, reinforcement learning, imitation learning, planning, large language models, vision-language models, world models, or multi-agent systems.
• Strong programming ability in Python and familiarity with modern machine learning frameworks such as PyTorch, JAX, or TensorFlow.
• Demonstrated ability to independently drive research from problem definition through experimentation, evaluation, and communication of results.
• Curious, rigorous, and comfortable working on open-ended research problems in a fast-moving environment. 

Preferred Qualifications:

• Research experience deploying robot learning systems on physical robots.
• Research experience with dexterous manipulation and humanoid robotics.
• Experience integrating LLMs or vision-language models with planning, control, memory, simulation, or real-world execution.
• Familiarity with robotics simulators such as Isaac Sim, MuJoCo, Habitat, ManiSkill, or equivalent platforms.
• Experience building end-to-end systems rather than working only on offline benchmark research.