Research Scientist - Robot Learning & RL

Acceler8; San Francisco (CA); Presencial; USD 207 000 - 253 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Resumo da oferta

Acceler8 Talent is recruiting a Research Scientist in San Francisco to advance robot learning and reinforcement learning. You will own original research and contribute to early directions spanning RL, embodied AI and human-robot collaboration.

You’ll develop policies with imitation learning, offline/online RL, build simulations for control and planning, and explore world models and adaptation across tasks. This role is fully onsite in Downtown San Francisco.

Qualificações

• PhD or equivalent original research in robotics/ML/CS.
• Strong publications at major venues (NeurIPS/ICML/ICLR/CoRL/RSS/IROS/ICRA).
• Deep RL knowledge and hands-on policy development.

Responsabilidades

• Develop policies using imitation learning, offline RL and online RL.
• Build and evaluate simulation environments for control and cooperation.
• Research world models, hierarchical policies and task adaptation.

Conhecimentos

Deep reinforcement learning

Python

PyTorch

Research publication track

Formação académica

PhD in robotics / ML / CS

Ferramentas

Simulation environments

Descrição da oferta de emprego

Research Scientist – Robot Learning & Reinforcement Learning

Full-time onsite, Downtown San Francisco

I’m working with a frontier AI research lab building low-latency models that help AI understand, act and cooperate with people in real time.

Starting with games and simulation, they’re researching how agents learn, coordinate and adapt alongside humans—and how these capabilities could transfer into robotics.

Supporters include Supercell, General Catalyst and South Park Commons, alongside participation in NVIDIA’s Inception Program.

This is an opportunity to own original research and help shape an early direction spanning reinforcement learning, embodied AI and human-robot collaboration.

You’ll work on:

• Developing policies using imitation learning, offline RL and online RL
• Building simulation environments and evaluations for control, planning and cooperation
• Researching world models, hierarchical policies and adaptation across tasks
• Exploring transfer from games and simulation into robotics
• Connecting learned policies with perception, memory and conversational behavior

Requirements:

• PhD in robotics, ML, computer science or equivalent original research experience
• Strong publications at venues such as NeurIPS, ICLR, ICML, CoRL, RSS, ICRA or IROS
• Deep reinforcement learning knowledge and hands-on policy development
• Strong Python and PyTorch skills
• Experience with simulation, embodied learning, robotic control or interactive environments
• Enthusiasm for exploratory research and full-time onsite work in San Francisco
• Imitation learning, world models, sim-to-real or human-AI cooperation experience is especially valuable.

Package: Up to $230K base salary + equity