Orbital is an AI-first industrial company building hardware from the atoms up. Our goal is to lead an industrial renaissance to advance critical technologies and secure our planet for generations to come.

We’re starting with critical hardware for AI data centers to make them more performant and sustainable. Every Orbital product is invented with our AI platform — uniting AI-automated hardware engineering with AI-designed material science to achieve breakthrough real-world performance.

We have an ambitious mission and need excellent people in all our teams - AI research, operations, advanced materials, mechanical engineering, chemical engineering and manufacturing.

Working at Orbital means working in tightly integrated, vertically integrated teams. We’re looking for people who have a love of physical technology, curiosity in AI and a desire to learn.

As a Senior Machine Learning Engineer at Orbital, you will define the technical direction for AI systems powering the multi-scale design of physical technologies. When we say multi-scale, we mean it: we build world-class foundation models for simulating both the microscopic motion of atoms and the macroscopic flow of liquids in 1GW data centers. We then co-design across these different scales using the ingenuity of our scientists and engineers, augmented with best-in-class domain agents.

In this role you will take ownership of ambiguous, high-risk, cross-functional projects, driving them from inception to completion while influencing the organisation’s broader technical strategy. First and foremost, we want to work with someone with a love of craftsmanship, continual learning, and building systems that scale. We also value low ego, and a genuine passion for using AI to solve major global industrial technology challenges.

Key Responsibilities

Define the technical bar and drive engineering excellence across teams

• Establish and maintain exceptionally high standards for code quality, system architecture and ML research and engineering practices across multiple teams, through hands-on coding, technical review and architectural leadership

• Design robust, well-engineered systems that set the standard for the organisation, balancing research velocity with production requirements

• Drive and influence technical decisions on model selection, training approaches and deployment strategies across the company’s ML efforts, articulating trade-offs to both technical and non-technical stakeholders

Own ambiguous, high-risk AI projects across the organisation

• Take ownership of ambiguous, high-risk, cross-functional projects, driving them from inception to completion and coordinating across multiple teams and domains

• Develop and deploy AI solutions across the entire technology development pipeline- computational chemistry simulations, agentic workflows and beyond with responsibility for end-to-end project outcomes

• Rapidly upskill in new technical areas through close collaboration with domain experts, and enable others to do the same (no prior chemistry or materials experience required)

• Perform sophisticated analysis and interpretation of complex datasets, communicating insights and their implications to influence organisational direction

Shape the frontier of ML research at Orbital

• Design and implement novel ML architectures for complex scientific domains, with work that meets publication standards at top-tier conferences and defines new research directions for the team

• Stay current with state-of-the-art research and contribute to research discussions, providing insights on new developments that influence the team’s research agenda

• Drive research projects from conception through to deployment, showing initiative, technical depth and the ability to make judgment calls on high-uncertainty problems

What We’re Looking For

• Extensive AI/ML research and software engineering experience, with a track record of leading complex projects and influencing technical direction — demonstrated through sustained research contributions, senior industry roles, or a combination of both

• Proven track record of leading and delivering high-impact, ambiguous ML/AI projects 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 that set organisational standards

• A strong ability to reason about algorithms, system design, linear algebra, probabilistic concepts and ML engineering trade-offs — and to communicate these effectively to influence technical direction

• An ability to debug complex machine learning systems through meticulous attention to detail, testing of edge cases and carefully selected ablations

• Demonstrated ability to mentor and develop engineers, with evidence of shaping others’ technical growth and career trajectories

• 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. Experience influencing technical strategy at an organisational level.

Orbital is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.