Orbital

Role Overview We're looking for a QA Engineer (Contract) to join our Product Engineering team and take ownership of quality on a new product currently in active development. You'll be hands on from day one, executing manual testing, building out automation coverage, and providing clear defect reporting that directly shapes engineering priorities. You'll get a chance to: Execute functional, regression, and exploratory testing across a greenfield product Design and maintain test cases and plans based on product requirements and engineering specs Build and maintain an automated test suite using tools like Playwright, Cypress, or similar Integrate automated tests into the pipeline for continuous quality feedback Log, triage, and track defects with clear reproduction steps, severity ratings, and supporting evidence Produce concise QA status reports for the Head of Product Engineering and the wider team Contribute to sprint ceremonies and influence acceptance criteria from a quality perspective You should apply if: You have proven experience in both manual and automated QA testing on software products in active development You're comfortable writing and maintaining automated test scripts (Playwright, Cypress, Selenium, or equivalent) You write clear, structured defect reports and test cases that engineers can act on immediately You have experience with defect tracking tools (Jira, Linear, or similar) and understand CI/CD pipelines You can work autonomously in a fast-moving environment with minimal hand holding It would also be nice if you have: Experience with API testing (Postman or similar) Familiarity with developer tools and log reading for debugging context Experience on an early stage or greenfield product Security is everyone's responsibility at Orbital. We ask all team members to follow our security policies, complete regular awareness training, and handle sensitive data with care in line with ISO 27001 standards. Spot something unusual? Reporting risks or incidents quickly helps us maintain the strong culture of security and compliance we all depend on. At Orbital, we're committed to building a diverse and inclusive team. We especially welcome applications from people who are traditionally underrepresented in tech. Even if you don't meet every single requirement, or if the right role isn't listed yet, we'd still love to hear from you. This hiring range is a reasonable estimate of the base pay range for this position at the time of posting. Pay is based on several factors, which may include job-related knowledge, skills, experience, and business requirements.

Orbital is seeking a QA Engineer (Contract) to join its Product Engineering team in Greater London. The successful candidate will take ownership of quality on a new product, executing manual testing and building automation coverage. Responsibilities include designing test cases, logging defects, and producing QA reports. Required skills include experience in manual and automated QA testing, knowledge of defect tracking tools, and comfort with CI/CD pipelines. A diverse and inclusive workplace is emphasized, encouraging applications from underrepresented groups.

Orbital, located in Greater London, is seeking a Senior/Staff AI Engineer to lead the AI architecture for their Orbital Copilot project. In this role, you'll tackle complex AI problems, mentor team members, and collaborate with the AI VP and legal experts. Ideal candidates have substantial hands-on experience with AI systems, a proven track record of scaling AI products, and excellent communication skills. The position offers flexibility and a competitive starting salary in a fast-growing tech environment.

We're on a mission to make real estate transactions smarter, faster, and friction-free. Real estate is the world's largest asset class, yet the legal processes and tools behind it remain slow, manual, and underinvested. Lawyers must review dense documents line by line and piece together information across silos, all while clients demand faster, more transparent due diligence. Orbital Copilot is the AI assistant built exclusively for commercial real estate law. Developed with former practicing real estate lawyers, it accelerates complex due diligence by up to 70% while delivering legal-grade precision. We've just raised a $60m Series B to accelerate our UK/US expansion. We're trusted by leading firms like Goodwin and BCLP to remove the busywork so legal teams can focus on what they do best: applying sharp legal judgment, delivering standout client service, and getting deals over the line faster. Working at Orbital means joining a team that's reimagining how real estate transactions get done - moving fast, working collaboratively, and giving people the ownership to make a real impact from day one. Our vision We're building the AI that runs real estate transactions end-to-end. Today, property deals depend on overworked lawyers buried in paperwork and reports - a system that's slow, expensive, and stuck in the last century. We're replacing it with autonomous AI that handles the work from instruction to completion, so deals move at the speed of decisions, not documents. Our mission Our mission is to help any professional or individual involved in a property transaction to properly understand what they are getting into, from the outset, before incurring legal fees. Our values We are Bold & Ambitious (changing an entire industry is hard!) We give Power to our People (we give exceptional people autonomy to succeed) We Question or Commit (we welcome debate, but love reaching quick decisions) and we Eat that Frog! (we take on the hardest thing first) Role Overview We are seeking an exceptional Senior/Staff AI Engineer to join us at a defining moment for the company and for AI itself. You'll be a senior technical leader on Orbital Copilot - our agentic AI system reshaping how commercial real estate transactions get done, and a force multiplier on the rest of the AI org. This is not a role for someone looking to transition into AI. We're looking for an engineer who has lived and breathed AI/ML for years, who has built and scaled AI or agentic products from zero to multi million ARR, and who has the scars and intuitions to prove it. You've shipped systems where model behaviour, evaluation rigour, and product instincts had to come together for the business to work - and you've done it in production, with real users, real money, and real consequences. We're looking for someone who has a clear, considered mental model of where this technology is heading, what genuinely matters, and what's noise. You're opinionated about agent design, evals, model selection, and the right level of abstraction - because you've earned those opinions the hard way. At Orbital, we leverage the most intelligent, bleeding edge models from leading AI labs like OpenAI and Anthropic, and we're building one of the most ambitious agentic systems in legal AI. You'll own technical direction on hard problems - multi agent orchestration, long horizon reasoning, retrieval and document understanding at scale, and the evaluation infrastructure that lets us move fast without breaking trust. You'll set the bar for the wider AI engineering team, mentor strong engineers, and partner closely with the VP of AI, AI product managers, real estate legal domain experts, and software engineers to take Orbital Copilot to the next order of magnitude. You will get a chance to: Set technical direction for Orbital Copilot's agentic architecture and the AI systems underpinning our next generation of products. Design, build, and scale complex AI features end to end - from research spike to production system serving high stakes legal workflows. Drive the hardest AI problems we face: multi agent systems, long context reasoning, retrieval augmented generation, document understanding pipelines, and frontier model integration. Build the evaluation, monitoring, and reliability infrastructure that makes shipping AI in regulated, high trust environments possible. Raise the bar on the AI engineering team - through code review, architecture review, mentorship, and the standards you set in your own work. Partner with the VP of AI, AI PMs, and legal domain experts to translate a strong product worldview into systems that move customer and business metrics. Make the calls on model choice, abstractions, and trade offs that compound across the company's roadmap. You should apply if: You have a deep AI/ML background with several years working hands on with modern AI systems - not adjacent to them. You have built and scaled an AI or agentic product from early stages to multi million ARR, and can speak concretely to the technical and product decisions that got it there. You have a track record of excellence - a clear, demonstrable bar reached in your work, research, or both. We want to see evidence you've operated at the very top of your field. You have a well formed thesis on where the frontier is going and what it means for product, and you bring that perspective to every decision. You are AGI pilled with real conviction and the technical chops to back it up in production. You've owned production AI systems through deployment, evaluation, monitoring, and continuous improvement and know what good looks like. You raise the bar for the engineers around you and have a track record of doing so. You have excellent verbal and written communication skills in English, and can make sharp, opinionated technical arguments land with both engineers and non engineers. It would also be nice if you have: Deep, hands on experience with frontier LLMs (OpenAI's GPT 5, Anthropic's Claude models, Google's Gemini) and agentic frameworks. Experience designing and operating multi agent systems or complex agentic architectures in production. A strong evaluation and experimentation discipline - you've built eval harnesses, offline/online evaluation pipelines, and you treat evals as a first class part of the product. Experience building high performance, distributed systems at scale. Proven expertise in building highly secure, fault tolerant APIs. Strong understanding of modern dev practices like 12 Factor, CI/CD, and observability tools such as Datadog or Prometheus. Exposure to GraphQL APIs and WebSockets for real time interactions. Any frontend experience. Benefits Competitive starting salary Matched pension contributions and equity options in a fast growing start up Flexible working hours and location 25 days paid holiday (plus bank holidays) Professional equipment and personal development budget along with training opportunities to learn and develop your skills Free lunch on Wednesdays and Deliveroo budget for late nights Cycle to work scheme Security is everyone's responsibility at Orbital. We ask all team members to follow our security policies, complete regular awareness training, and handle sensitive data with care in line with ISO 27001 standards. Spot something unusual? Reporting risks or incidents quickly helps us maintain the strong culture of security and compliance we all depend on. At Orbital, we're committed to building a diverse and inclusive team. We especially welcome applications from people who are traditionally underrepresented in tech. Even if you don't meet every single requirement, or if the right role isn't listed yet, we'd still love to hear from you. This hiring range is a reasonable estimate of the base pay range for this position at the time of posting. Pay is based on several factors, which may include job related knowledge, skills, experience, and business requirements.

Orbital uses AI to build data center hardware that outperforms the competition. Our AI simulates materials at the atomic level, tests millions of hardware configurations in the time traditional methods test hundreds and finds optimal designs - not just good ones. The result is shipped hardware with specs incumbents can't match: 1 MW/rack, PUE Every deployment generates field data that improves our AI models. Better models produce better hardware. Better hardware enables more capable AI. The loop is already closed and tightening with each build cycle. We are not just beneficiaries of AI progress, we are a lever on its rate. Data Centers are where we start, because the market is urgent and the specs are demanding. But the AI-accelerated development process we've built - materials discovery, hardware design, manufacturing optimization - applies to any complex physical system. Data Centers are the proof point, not the ceiling. We have sites in London, Canada and the USA, building teams across ML research, Product development, Mechanical engineering, and Chemical engineering. If you want to work where AI meets atoms, we'd like to hear from you. As a Staff 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 ONE of: 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. Orbital is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

A cutting-edge technology company in the United Kingdom seeks a Staff Machine Learning Engineer to architect AI systems for the multi-scale design of physical technologies. This role involves setting high technical standards, delivering impactful AI projects across various domains, and ensuring the implementation of innovative ML architectures. Candidates should have significant experience or a PhD in relevant fields, a passion for AI, and a desire to contribute to transformative scientific advancements.

A leading hardware solutions provider in Greater London is seeking a Content Writer to produce high-impact content for various formats. The ideal candidate will have outstanding writing and editing skills, engage with technical audiences, and contribute to creating a foundational content function from scratch. Flexibility in remote work and a competitive compensation package are part of the offer. Join a dynamic team at the forefront of technology and innovation, shaping the future of materials and their applications.