Machine Learning Engineer
Required skills
Significant software engineering and ML experience, with depth in training, evaluating and deploying AI models Proven experience training, evaluating and productionising AI models at scale, with deep understanding of the full ML lifecycle from research to deployment Strong engineering fundamentals with the ability to write high-quality, maintainable code and architect robust systems A strong ability to reason about algorithms, system design, linear algebra, probabilistic concepts and ML engineering trade-offs An ability to debug complex machine learning systems through meticulous attention to detail, testing of edge cases and carefully selected ablations A genuine interest in building AI systems that enable breakthrough scientific and industrial applications
Preferred skills
Experience with physics-informed or chemistry-focused AI applications Experience building or fine-tuning large language models Experience with agent-based systems, tool use or agentic workflows Contributions to open-source ML projects or published research
Technologies
computational chemistry simulations, agentic workflows, foundation models, generative AI, scientific machine learning
Responsibilities
Set the technical bar and ensure engineering excellence Establish and maintain exceptionally high standards for code quality, system architecture and ML research and engineering practices through hands-on coding and technical review Design robust, well-engineered systems that others can build upon, balancing research velocity with production requirements Drive technical decisions on model selection, training approaches and deployment strategies Deliver high-impact AI projects across diverse domains Develop and deploy AI solutions across the entire technology development pipeline- computational chemistry simulations, agentic workflows and beyond Rapidly upskill in new technical areas through close collaboration with domain experts (no prior chemistry or materials experience required) Demonstrate strong implementation skills through hands-on development, contributing significantly to the codebase Balance research rigour with pragmatic engineering to deliver production-ready systems at scale Push the frontier of ML research Design and implement novel ML architectures for complex scientific domains, with work that meets publication standards at top-tier conferences Drive research projects from conception through to deployment, showing initiative and technical depth Engage continuously with the latest ML literature, staying current with developments in foundation models, generative AI and scientific machine learning
Seniority
Not specified
Domain
AI, machine learning, computational chemistry, materials science, industrial technology, data centers