Reinforcement Learning Researcher (Offline RL) - Robotics

envision recruitment • San Francisco Bay Area

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AI Summary

Develop cutting-edge offline reinforcement learning (RL) algorithms to advance real-world industrial robotics. Own research in goal-conditioned RL, policy representations, and model-based RL, with direct deployment on physical robots. Requires PhD in RL or exceptional Master’s with strong publication record in foundational RL research.

Key Highlights

Ownership of offline reinforcement learning (RL) research for industrial robotics

Explore goal-conditioned RL, value learning, policy representations, and model-based RL

Research directly deployed on real robots in a high-growth SF-based startup

High talent bar with 6-day onsite workweek in San Francisco

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Key Responsibilities

Develop and improve offline reinforcement learning (RL) methods for industrial robotics

Research and implement goal-conditioned RL, value learning, policy representations, and model-based RL

Translate theoretical RL research into practical applications on real-world robots

Collaborate with a team of AI experts from top-tier companies (e.g., OpenAI, Zoox) to advance robotics automation

Technical Skills Required

Reinforcement Learning Offline Reinforcement Learning Machine Learning Research

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Benefits & Perks

$200,000–$350,000+ annual salary

Competitive equity

Relocation and visa sponsorship available

Job Description

Build intelligent robots to automate industrial labour.

How great does that sound!?

Envision Recruitment are supporting an SF-based robotics startup on a mission to build robots capable of performing real-world industrial tasks.

They've recently raised $20m+ from prestigious investors and assembled a team drawn from OpenAI, xAI, Zoox and other leading tech companies.

They're now looking for a Reinforcement Learning Researcher to take ownership of their offline RL research.

You'll be developing new approaches to how robots learn, adapt and perform, exploring areas such as goal-conditioned RL, value learning, policy representations and model-based RL.

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