Job description

We’re searching for an experienced reinforcement learning engineer to join our team at GatlingX to train reinforcement learning agents and improve the MLOps infrastructure in order to unlock general AI Agent Capabilities. As an early GatlingX hire, you will establish the foundation of our GPU-EVM RL training gym (Similar to NVIDIA Issac Gym) to improve our current agent capabilities.

🪖 Responsibilities

Establish and lead the implementation of GPU-EVM RL training gym
Develop and set up training benchmarks, focusing on real application use cases.
Monitor system performance, proactively identify issues, and ensure overall health and security.
Stay updated with the latest reinforcement learning and AI agent trends, applying them continually to improve our processes.
Create and maintain technical documentation for internal and external use.
Balance speed and quality, with a focus on tangible results.

Requirements

Strong background and proven experience in Reinforcement Learning.
Good understanding of the current AI agent landscape (e.g. AutoGPT, babyAGI, SIMA)
Deep demonstrable understanding of reinforcement learning full-stack
Proven record of shipping features on time and on budget
Flexible and focussed on solutions
Exceptional problem-solving abilities and meticulous attention to detail.
Strong collaborative skills, with the ability to lead projects and work in a fast-paced setting.
Organized and self-sufficient

Bonus

Published RL/AI/MLOps papers in the past
Experience in Web3, Particularly Ethereum and even better if you know security
Experience building low-level tools
Experience as an early eng team member growing a product from 0 to 1
Design chops
Have managed other engineers
Have been a founder before

Benefits

Ground floor opportunity - depending on the candidate, this role has strong potential to turn into a leadership position
Technically challenging, cutting edge and intellectually stimulating product
Competitive compensation and benefits
Major industry conferences and events covered
Remote-first, with a hub in the UK
Flexible work schedule

Required profile