2 Positions: Machine Learning Engineer & Machine Learning Researcher
Candidates should take the time to read all the elements of this job advert carefully Please make your application promptly.
My client is a scaling robotics start-up that is creating autonomous industrial robots capable of handling intricate, dexterous tasks. Theyre 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.
Youll 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