Machine Learning Engineer (Mid-Snr)

A dynamic engineer that enjoys translating real-world challenges into machine learning problems and learning directly from the customer. You will have ownership of technology development and implementation across industrial sites, helping bridge machine learning, deployment, industrial operations, and product direction. This is a hybrid role combining hands-on technical contributions and occasional forward-deployed work at customer sites with scope for leadership and strategic responsibilities. You’ll contribute across modelling, infrastructure, deployment, experimentation, and technical decision-making., * Work closely with founders, engineers, and industrial partners on real-world AI deployments

• Build systems used by some of the world’s largest steel manufacturers to improve efficiency and reduce emissions
• High ownership environment with influence over technical direction and deployment strategy
• Collaborative and supportive technical team culture
• London based office, walking distance from Soho and Green Park

Interview Process

• Introductory conversation with one of the cofounders
• Technical Interview
• Meet the Team
• Collaborative working session/ discussion around real world problems
• Offer

• Experience with reinforcement learning
• Customer facing experience
• Proven end to end design and implementation of ML systems in real world applications., * Strong experience building ML systems in Python using frameworks such as PyTorch, JAX, or similar
• Experience working with complex time-series, sensor, or industrial process data
• Strong understand of production-grade software engineering practice, including version control, CI/CD, containers, and cloud platforms such as GCP, AWS, or Azure
• Comfortable working across modelling, infrastructure, experimentation, and deployment

Leadership & Collaboration

• Strong communication skills with the ability to explain complex technical concepts clearly
• Ability to work cross-functionally across engineering, product, operations, and customer environments
• Comfortable balancing technical decisions with real-world operational and business constraints
• Maintains clear documentation and a strong engineering mindset around reliability and maintainability