Orbital

Orbital is seeking a candidate to lead the development of key product features, utilizing AI to transform real estate transactions. You will guide small teams, make technical decisions, and work with modern frameworks like TypeScript and Python. The position offers a hybrid working environment in Farringdon, competitive salary with equity options, and a focus on impactful contributions in a fast-paced startup.

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. Orbital's internal AI software platform, Curie, is used by our materials scientists, hardware engineers and manufacturing engineers to design and build our 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 Curie. 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 the Curie platform, 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.

Orbital is seeking a Backend Engineer to design and operate the core systems powering Curie, integrating AI models and building scalable APIs and data pipelines for our AI-first industrial platform. You will collaborate with ML researchers and hardware/domain experts, own features end-to-end, and contribute to high engineering standards through testing, monitoring and thoughtful design.

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.

A cutting-edge AI startup is seeking a Product Manager to lead the development of AI-driven products that transform real estate transactions. The ideal candidate will have a passion for building impactful products, a strong curiosity about AI, and excellent collaboration skills. Offering competitive salary, equity options, and flexible working arrangements, this role is perfect for someone eager to make a significant impact in a fast-paced environment.

Role Overview We're hiring our second SRE to help scale our platform and reliability efforts. This role offers a chance to design, implement, and manage our infrastructure, CI/CD pipelines, and production operations from the ground up. You'll have autonomy in shaping our tech stack, defining best practices, and building scalable systems that will set the foundation for future engineering growth. If you thrive in startup environments and enjoy the blend of software engineering, operations, and infrastructure, we'd love to hear from you. What You'll Get Set Up and Manage Infrastructure Design, build, and maintain a robust, cloud-based infrastructure on Azure. Develop and maintain infrastructure as code using tools like Terraform. Have ownership of our system's reliability and scalability. Deploy and Orchestrate Containers Use k8s and Docker to manage containerised applications, ensuring high availability, scaling, and resource optimisation. Set up and manage k8s clusters to support reliable and scalable infrastructure. Develop CI/CD Pipelines Design and implement CI/CD pipelines to automate build, test, and deployment processes. Collaborate with development teams to streamline code integration and ensure high-quality releases. Implement Monitoring and Incident Management Set up proactive monitoring, logging, and alerting systems to detect and resolve issues before they impact users. Develop and refine incident response protocols and conduct root cause analyses. Foster Collaboration and Knowledge Sharing Work closely with cross functional teams, especially software engineering, to instil and grow a DevOps culture. Document processes, systems, and configurations to ensure scalability and facilitate knowledge sharing. Requirements Experience and Technical Skills 4+ years in a DevOps, SRE, or related role with hands on infrastructure experience. Expertise with a major cloud provider, preferably Azure. Proficiency with Infrastructure as Code tools and Kubernetes ecosystem tools such as Terraform, Kubernetes, and FluxCD. Solid experience with Docker and Kubernetes for container management. Knowledge of CI/CD tools (Jenkins, GitLab CI, CircleCI, GitHub Actions) and experience setting up automated workflows. Familiarity with monitoring and logging tools (Prometheus, Grafana, ELK stack, or DataDog). Strong scripting skills (Python, Bash, or similar) for automation. Software Engineering Experience Minimum 2 years of professional experience in software engineering, with a focus on Python. Proficiency in writing clean, scalable, efficient Python code following best practices. Python Development Expertise Strong understanding of Python libraries, frameworks, and modules relevant to SRE such as Flask, Django, or FastAPI. Experience with Python's async capabilities and asynchronous programming for performance optimisation. Motivation and Ownership Ready to take ownership of critical systems and be the go to person for system reliability. Enjoy solving complex technical challenges and proactively seek solutions. Collaboration and Communication Effective communication with stakeholders, clear documentation of processes, and ability to explain technical concepts to non technical team members. Benefits Competitive starting salary of £82,000 - £104,000 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, including training opportunities Cycle to work scheme An inclusive community with all company off sites, lunches, and socials 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 helps us maintain a strong culture of security and compliance. 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.