Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable abundance for a growing planet.
The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls.
We’ve raised over $32 million, including one of the largest seed rounds for an AI company in the UK. We’re just getting started
The role
As a Senior Full Stack Engineer on the Core AI team, you will build the platform that makes Orbital a scalable product. You will own the internal application layers, developer facing APIs, shared UI components, and integration frameworks that power every Orbital deployment.
This is a product engineering role focused on building reusable systems. You will collaborate with AI research, Infra, and software teams to translate complex industrial workflows into reliable system components and intuitive user interfaces. Your work will define how users interact with Orbital across control rooms, engineering teams, and cloud-based environments.
What Success Looks Like
- Orbital ships with high quality dashboards and platform interfaces that users rely on every day.
- Core APIs and microservices provide stable contracts for all AI, data, and automation features.
- The front-end and back-end codebase is modular, well structured, and easy for new engineers to contribute to.
- Releases are frequent, reliable, and backed by strong CI and CD pipelines.
- Features scale from one deployment to many without rework or site specific engineering.
Job Requirements
- Software development fundamentals: complexity, data structure, algorithms.
- Understanding of system design patterns and ability to make informed architectural decisions.
- Python for scripting, APIs, and data integration.
- Solid back-end engineering skills with Node.js, FastAPI, Express or similar framework.
- Experience building containerised microservices using Docker and Kubernetes.
- Familiarity with distributed systems, message brokers like Kafka or RabbitMQ, and event driven architectures.
- Proficiency working in Linux environments for debugging, deployment, and performance tuning.
- Hands‑on experience with AWS services including EKS, S3, IAM, CloudWatch and networking fundamentals.
Product and Engineering Mindset
- Comfortable working in a fast‑paced product environment with evolving requirements. We ship a new release every 2 weeks
- Ability to collaborate deeply with AI, Infra, and Software teams to build scalable long‑term features.
- Strong focus on reliability, resilience, and code quality.
- Ability to design systems that scale across many customers and industrial verticals.
Job Responsibilities
1. Application Development
- Build features across the product stack, spanning data storage and access, backend services, UI layers, and API endpoints.
- Develop stable backend logic and data access layers that expose forecasting, physics models, and LLM outputs as product features.
- Create internal tools, workflows, and interfaces that allow deployment to different engineering use cases without requiring site specific customisation.
- Ensure all application layers meet strict standards for reliability, security, observability, and performance in both cloud and on‑prem environments.
2. Platform and Microservices Architecture
- Design and implement containerised microservices that run inside Kubernetes clusters.
- Create shared libraries and services that enable multiple Orbital verticals to reuse common logic.
3. Core Product Engineering
- Collaborate with Product, ML Engineering, and Infra teams to define product requirements and long‑term architecture.
- Build the interface layer that exposes AI inference, and physics‑based outputs to users as stable platform features.
- Implement platform abstractions for data ingestion, inference, scheduling, and monitoring.
- Establish design patterns, API contracts, and engineering standards that unify the Orbital ecosystem.
4. Software Engineering Best Practices
- Write clean, modular, maintainable code across front‑end and back‑end systems.
- Set up CI and CD pipelines for test coverage, automated QA, and rapid release cycles.
- Conduct code reviews, architectural reviews, and contribute to engineering guidelines.
- Leverage modern agentic coding tools to write predictable and maintainable code at high speed.
Competitive salary and benefits
Ability to work from the office or remote
Employment contract in UK or India, EOR or contractor options available in other jurisdictions
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