---
title: 'Machine Learning Engineer at Orbital'
canonical: 'https://feeny.ai/job/machine-learning-engineer-orbital-london-2agc9mw9xeey'
type: 'job'
last_seen: '2026-09-05'
---

# Machine Learning Engineer at Orbital

- **Company:** Orbital
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-02-16
- **Last confirmed live:** 2026-09-05
- **Apply:** https://jobs.ashbyhq.com/orbitalindustries/0405b3d7-c134-43e4-a7af-a9763a5bf3d9

## Job description

Orbital Industries is an AI Industrial company, with frontier AI embedded at every step in the production of critical physical products - from creating advanced materials to engineering and manufacturing.

Every Orbital Industries product is developed using CurieOS, our AI operating system, uniting AI-automated hardware engineering with AI-designed material science to achieve breakthrough real-world performance. We also offer CurieOS to our customers and partners, extending the same platform and workflows that power Orbital Industries to their own teams and products.

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 Industries means working in vertically integrated teams across the full stack, from molecules to manufacturing. We're looking for people who have a love of physical technology, curiosity in AI and a desire to learn.

As a Machine Learning Engineer at Orbital, you will architect cutting-edge AI systems for 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 set exceptionally high technical standards and drive projects from prototype through to production deployment. 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

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

## What We're Looking For

- Significant software engineering and ML experience, with depth in training, evaluating and deploying AI models - demonstrated through industry work
- 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.

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

## About Orbital

## Company Overview
- **One-liner**: Orbital Industries is an AI-first industrial company that designs and manufactures physical products using its own AI platform, starting with advanced hardware for AI data centers.
- **Entity Type**: Private (Funding stage not disclosed)
- **Headquarters**: London, United Kingdom
- **Founded**: 2022
- **Founders**: Jonathan Godwin (CEO)

## Core Business
- **Primary Industry**: Manufacturing / Industrial AI / Data Center Hardware
- **Target Customers**: B2B, Enterprise (specifically data center operators)
- **Mission/Purpose**: To lead an industrial renaissance by advancing critical technologies and securing the planet, using AI to accelerate the invention and manufacture of physical products.

## Products & Services
- **Orb (AI Platform)**: An integrated AI foundation model and agentic operating system that unites domain-specific AI across material science, fluid dynamics, and hardware engineering to discover and design novel physical products. It powers all of the company’s hardware development.
- **AI Data Center Products**: High-performance, sustainable hardware for AI data centers, including direct-to-chip and two-phase liquid cooling systems using newly discovered molecular classes for high-density GPU cooling. These are sold as modular data center components.

## Market Standing
- **Valuation / Market Cap**: Not publicly available.
- **Key Metric**: Headcount is approximately 58 employees (as of mid-2026), with 12 active job postings indicating continued growth.
- **Notable Investors/Partners**: No specific investors named, but the company highlights a relationship with **NVIDIA**. CEO Jonathan Godwin discussed the company with Jensen Huang at an NVIDIA event, and Orbital is part of the beta release for NVIDIA's Alchemist toolkit.
- **Growth Signals**: 
  - Rapid initial headcount growth (currently ~58 employees).
  - 12 open positions across ML engineering, mechanical engineering, finance, and talent.
  - Active in the Open Compute Project community.
  - Strong LinkedIn follower growth (+3.9% monthly).

## Competitive Advantages
- **AI-Native Hardware R&D**: Unlike traditional hardware companies with large, siloed R&D departments, Orbital operates like an AI lab with small, interdisciplinary teams that use AI agents, robotic labs, and physical engineering sites.
- **Foundation Models for Physics**: Possesses world-leading AI models for advanced materials, fluid dynamics, and other physical sciences, enabling rapid iteration in simulation ("in silico") before physical prototyping.
- **Sample-Efficient AI**: Their "autoresearch" agents are designed to find correct answers in as few experiments as possible, dramatically reducing the time and cost of physical R&D, which is typically measured in months and millions of dollars per experiment.

## Strategic Focus
- **Geographic Expansion**: Scaling operations with offices already established in London (HQ), San Francisco, and Calgary. Hiring a Head of Sales for the EMEA region.
- **Broader Industrial Ambition**: While starting with data center cooling, the strategy is to apply its AI platform to other critical physical sectors, including energy, semiconductors, and general materials and machinery.
- **Talent Acquisition**: Actively hiring ML researchers and engineers to build the "next frontier of industrial AI," alongside core engineering and business roles.

## Why Work Here
- **Culture**: Described internally as a "talent-dense" team operating like a frontier AI lab, but with the tangible output of physical products. This appeals to those who want to see their code impact the real world.
- **Work Model**: Hybrid (typical time on-site varies by role and office location). Roles in San Francisco are in-office; London roles are hybrid.
- **Notable Perks/Engineering Culture**: Engineers work on cutting-edge problems at the intersection of AI and physics. The company is small but has high-profile ambitions and partnerships (e.g., NVIDIA), offering significant ownership and impact.

## Sources
1. [orbitalindustries.com](https://www.orbitalindustries.com/)
2. [orbitalindustries.com/about](https://www.orbitalindustries.com/about)
3. [linkedin.com/company/orbitalindustries](https://www.linkedin.com/company/orbitalindustries)
4. [builtin.com](https://builtin.com/company/orbital-industries)
5. [jobs.ashbyhq.com/orbitalindustries](https://jobs.ashbyhq.com/orbitalindustries)

## Other roles at Orbital

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