Senior Machine Learning Engineer

Cubiq Recruitment
City of London, England
13 months ago
Applications closed

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2 Positions: Machine Learning Engineer & Machine Learning Researcher


My client is a scaling robotics start-up that is creating autonomous industrial robots capable of handling intricate, dexterous tasks. They're looking for Senior ML Engineers who want the freedom to bridge the gap between state-of-the-art research in embodied AI and real-world systems.

You'll be joining a team of accomplished roboticists with excellent PhDs, high-impact research, and industrial know-how, who will help nurture and develop you to the next level!


Salary: Up to £150k

Location: London

Onsite: Most days


What You’ll Be Doing (Machine Learning Engineer):


  • Implement and scale state-of-the-art AI algorithms to be deployed on real-world robotic systems
  • Build robust data pipelines and work with multi-modal sensor data
  • Collaborate with a talented team of researchers to improve and refine algorithms for real-world tasks
  • Ensure the scalability and maintainability of production systems, with a strong focus on (low-level) MLOps


What You’ll Be Doing (Machine Learning Researcher):


  • Conduct groundbreaking research to develop new algorithms and models for robotics
  • Work on advanced learning approaches such as reinforcement learning and imitation learning
  • Contribute to creating multi-robot systems and cross-embodiment learning techniques
  • Drive innovation by publishing research and attending top conferences


Engineer Background:


  • Expertise in PyTorch or TensorFlow, distributed computing, and multi-GPU training
  • Robotics experience, particularly in reinforcement/ imitation learning
  • Experience in cloud infrastructure (AWS, GCP, or Azure) and containerization
  • Ability to translate research papers into deployable systems


Researcher Background:


  • PhD or advanced degree in Robotics, Machine Learning, or a similar field
  • Experience with top-tier conferences like CoRL, ICRA, RSS, ICML, ICLR, NeurIPS
  • Strong knowledge of reinforcement learning, imitation learning, and real-world deployment

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