ML Ops Engineer, Materials Nexus

• Minimum 1st degree in Computer Science, Physical Science or a related field, or equivalent work experience
• 2+ years experience building and deploying ML products in a team
• Experience applying machine-learning and data science techniques to numerical problems (e.g., use of TensorFlow to train models of physical properties)
• Experience using development collaboration tools for large multi-developer projects (e.g., git)
• Strong understanding of the mathematical foundations of machine-learning and data science
• Understanding of scientific principles, their application in deep-tech and scale of impact in the world
• Appreciation of early-stage startups and the opportunities to expand your skillset
• Comfortable quickly adapting to new situations, learning new skills, and working independently as a self-starter
• Inquiring mind, aptitude for creative thinking and problem solving, and proactive, methodical, and organised approach
• Excellent communicator, able to effectively convey information to technical and non-technical colleagues in both written and verbal formats
• Proven track record of ability to work effectively in a team
• Appreciation of the needs, priorities, and challenges faced by a start-up company

Desirable

• We are looking for a talented ML engineer to play a leading role in our ML sub-team, complimenting the current team’s expertise and expanding our platform’s capabilities in exciting new directions
• Contribute to the design and implementation of general-purpose ML architecture and tools to be used by the ML team to build state-of-the-art ML models of materials
• Apply data science and machine-learning to infer understanding from our datasets and predict novel materials
• Collaborate with our science team to identify goals for platform development, opening new avenues for scientific investigation
• Take on more responsibility and control as we grow as a company and scale up our technology

• Three stage interview process
• Initial 45 min video call "get to know" by our Chief Scientist, Jon Pillow
• 1.5 hour in-person interview, at our London offices, to go into detail around tech & science and a chance to meet the broader team
• A final 30 minute culture interview led by our CCO, Nic Stirk
• Successful candidates will need to complete:
• Belbin teams role profile (personality profile of which you’ll receive a full report)
• References from two separate employers
• DBS check