Orbital - Staff Machine Learning Researcher
Requirements
• 5+ years of professional experience in ML/AI research or engineering. • A relevant PhD + 2 years of professional experience in ML/AI research or engineering. • 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 • Upon reading Hamming's You and Your Research, you resonate with quotes such as: • "Yes, I would like to do first-class work" • "You should do your job in such a fashion that others can build on top of it, so they will indeed say, 'Yes, I've stood on so and so's shoulders and I saw further.'" • "Instead of attacking isolated problems, I made the resolution that I would never again solve an isolated problem except as characteristic of a class" • Bonus: 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.
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
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