---
title: 'Senior Backend Engineer at Orbital'
canonical: 'https://feeny.ai/job/senior-backend-engineer-orbital-london-hjbxs88h2eyt'
type: 'job'
last_seen: '2026-09-12'
---

# Senior Backend Engineer at Orbital

- **Company:** Orbital
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-25
- **Last confirmed live:** 2026-09-12
- **Apply:** https://jobs.ashbyhq.com/orbitalindustries/1f9f61ca-6b41-4628-a663-5f6d1a15f51b

## 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. Orbital's AI software platform, CurieOS, is used by scientists and engineers to design and build products serving critical industries. Powering this software are our world leading AI models in advanced materials, hardware engineering and simulation. As a Backend Engineer at Orbital you will design, build and operate the core systems powering CurieOS. You will work across the full backend stack: APIs, data pipelines, graph databases, event-driven architectures and the infrastructure that connects our AI models to the tools our scientists and engineers use daily. An example of a feature you might build is a unified context graph across materials, engineering and manufacturing data for our AI agents. First and foremost, we want to work with someone with a love of craftsmanship, continual learning and building systems that scale.

## Key Responsibilities

Build and operate core backend systems

- Design and implement APIs, services and data pipelines that power CurieOS, with a focus on reliability, performance and clean abstractions
- Build and maintain integrations between our AI models, scientific tools and internal workflows
- Own the full lifecycle of backend features from design through deployment, monitoring and iteration

Drive engineering quality

- Write well-tested, maintainable code and contribute to a culture of high engineering standards through code review, documentation and technical discussion
- Improve system observability, reliability and performance — instrument, monitor and optimise the systems you build
- Make pragmatic technical decisions that balance speed of delivery with long-term maintainability

Collaborate across the team

- Work closely with ML researchers, product engineers and domain experts (materials scientists, hardware engineers) to understand their needs and translate them into robust backend solutions
- Contribute to architectural decisions and help shape the technical direction of the platform
- Share knowledge, mentor peers and help establish best practices as the team grows

## What We’re Looking For

- Backend engineering experience with strong programming skills
- Proven experience designing, building and operating backend systems in production — APIs, data pipelines, event-driven architectures or similar
- Strong fundamentals in at least one backend language (e.g. Python, Go, Rust, Java/Kotlin) and comfort working across the stack when needed
- Experience with databases (relational and/or graph), message queues, caching layers and cloud infrastructure
- A track record of shipping and iterating on software that real users depend on, with a strong sense of what makes systems reliable and maintainable
- The ability to reason about system design, data modelling and engineering trade-offs — and to communicate these effectively
- An ability to debug complex distributed systems through meticulous attention to detail, structured investigation and carefully chosen instrumentation
- A genuine interest in building software that enables 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: Previous experience working in an AI/ML environment, familiarity with the workflows, tooling and pace of AI teams is a real advantage. Experience with graph databases, knowledge graphs or scientific data platforms. Experience with infrastructure-as-code, containerisation (Docker/Kubernetes) or CI/CD pipelines. 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)

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