Independently design, implement and benchmark high-performance ML models
Develop novel deep learning architectures for complex scientific domains, with work that meets publication standards at top-tier conferences
Demonstrate strong implementation skills through hands-on development in PyTorch and related frameworks
Drive research projects from conception through to deployment, showing initiative and technical depth
Collaborate effectively with computational and experimental scientists, translating domain requirements into ML solutions
Demonstrate intellectual curiosity and the ability to quickly master new scientific domains outside your immediate expertise
Bridge the gap between foundational ML research and practical applications in materials science
Build robust, well-engineered codebases that others can build upon, balancing research velocity with code quality
Engage continuously with the latest ML literature, staying current with developments in foundation models, generative AI and scientific machine learning
Contribute to establishing technical best practices and knowledge sharing within the research team
Requirements
PhD or 3-5 years of experience in machine learning research
A strong ability to reason about algorithms, data structures, linear algebra and probabilistic concepts
A strong ability to debug complex machine learning systems through meticulous attention to detail, testing of edge cases and carefully selected ablations
An appreciation for high-quality code and software engineering practices
A genuine interest in pursuing AI-driven scientific discovery and building computational tools that enable breakthrough materials research
Bonus: Experience working with graph neural networks, message passing neural networks, or computational methods on graphs, 3D point clouds, or other 3D data. Experience training, evaluating or building with large language models.
Benefits
Competitive salary commensurate with AI sector
Flexible and generous paid time off
Excellent health, dental and vision insurance plan
Equity package - the ability to own part of Orbital Materials as we grow
Regular company offsites to the USA and beyond
The experience of working in a cutting-edge organisation dedicated to a better future
Clear pathways to technical leadership and ownership of research domains as we scale
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