Deep Reinforcement Learning Research Engineer

About Nimble

Nimble is a robotics and AI company building end-to-end autonomous logistics to enable fast, efficient, and sustainable commerce. We're developing generalized robot intelligence and building general-purpose logistics robots, the first in the world capable of performing all core warehouse functions.

Our mission is to empower and inspire mankind to accomplish legendary feats by inventing robots that liberate us from the menial.

Our long-term vision is to invent the Autonomous Supply Chain -- everything from the inside of factories and warehouses to your front door -- using next-gen intelligent robotics to deliver faster, more efficient, and more sustainable commerce.

We were founded from the AI labs at Stanford and Carnegie Mellon. We're backed by the World's most prestigious investors as well as AI and robotics legends like Marc Raibert (Founder of Boston Dynamics), Fei-Fei Li (Chief Scientist of AI at Google, Director of Stanford's AI lab), and Sebastian Thrun (Founder of Google X, Waymo, Kitty Hawk, and Udacity)! We've recently closed a $106M Series C funding round with a $1 billion valuation and we're continuing to grow our all-star team.

Link: Introducing Nimble -- Intelligent Fulfillment Robots at Scale

Nimble's Core Values:
Be relentlessly resourceful - Challenge the status quo. Make the impossible, possible. Do whatever it takes.
Be legendary - Be the best at whatever it is that you do. Leave your mark. Motivate and inspire others.
Be humble - Leave your ego behind. Have a growth mindset. Learn and improve yourself every day.
Be dependable - Take ownership and deliver. Be loyal to your team and Nimble's mission.

The Role

Nimble is looking for a Deep Reinforcement Learning Research Engineer to help us advance our robotics moonshot by designing, developing, implementing reinforcement learning algorithms controlling multiple robots for a stealth project.

You will play a critical role in working with our all-star cross-functional team to build models with immediate applications for the world's biggest robotics opportunity.

Responsibilities

• Design, and implement your own reinforcement learning models towards the end goal of controlling our fleet of robots.
• Welcome new challenges and work independently across disciplines to both investigate issues and identify solutions.
• Design, implement and evaluate models, agents and software prototypes of perceptual processing.
• Report and present research findings and developments including status and results clearly and efficiently verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.

Qualifications

• Ph.D. in Deep Reinforcement Learning. Experience with multi agent systems is preferred.
• Strong track record of publishing papers and/or deploying real-world applications
• Experience with system architecture, design and development
• Relevant experience to the position such as post doctoral roles, a proven track record of publications.