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
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 interfaces that users rely on daily
- Core APIs and services provide stable, well-documented contracts for AI, data, and automation features
- Frontend and backend codebases are modular, well-structured, and easy to extend
- Releases are frequent, reliable, and backed by strong CI/CD pipelines
- Features scale from one deployment to many without site-specific rework or bespoke
engineering
Requirements
- Strong software engineering fundamentals (data structures, algorithms, complexity)
- Solid understanding of system design patterns and architectural trade-offs
- Strong proficiency in JavaScript / TypeScript
- Experience building complex applications in React or Svelte
- Ability to design reusable UI components and frontend architecture
- Python for APIs, orchestration, and data integration
- Backend experience with Node.js, FastAPI, Express, or similar frameworks
- Experience designing and maintaining API-driven systems
- Experience building containerised microservices using Docker and Kubernetes
- Familiarity with distributed systems and event-driven architectures
- Experience with message brokers such as Kafka or RabbitMQ
- Comfortable working in Linux environments for debugging and performance tuning
- Hands-on experience with AWS (EKS, S3, IAM, CloudWatch, networking fundamentals)
Product & Engineering Mindset
- Comfortable operating in a fast-moving product environment with evolving
requirements
- Strong collaboration skills across AI, infra, and product teams
- Bias toward reliability, resilience, and long-term maintainability
- Ability to design systems that scale across customers, sites, and industrial verticals
- Comfortable shipping on a bi-weekly release cadence
What This Role Is Not
- Not a pure frontend or UI-only role
- Not infrastructure-only or DevOps-focused
- Not a research or modelling role
- Not a one-off project engineer; this role builds core platform primitives
1. Full Stack Product Development
- Build features across the product stack: data access, backend services, APIs, and UI
layers
- Develop backend logic that exposes:
o Time-series forecasts
o Physics-based model outputs
o LLM and agent outputs
As stable product features
o Reliability
o Security
o Observability
o Performance
Across cloud and on-prem environments
2. Platform & Microservices Architecture
- Design and implement containerised microservices running in Kubernetes clusters
- Create shared services and libraries that enable reuse across Orbital verticals
- Define API boundaries and service contracts that allow AI and infra layers to evolve
independently
3. Core Platform Engineering
- Collaborate with Product, AI Research, ML Engineering, and Infra teams on:
o Product requirements
o Long-term platform architecture
- Build interface layers that expose inference, physics-based reasoning, and optimisation
as first-class platform capabilities
- Implement abstractions for:
o Data ingestion
o Inference orchestration
o Scheduling
o Monitoring and health checks
- Establish design patterns and engineering standards that unify the Orbital ecosystem
4. Software Engineering Excellence
- Write clean, modular, maintainable code across frontend and backend systems
- Set up and improve CI/CD pipelines for:
o Automated testing
o QA
o Rapid, safe releases
- Participate in code reviews and architectural discussions
- Leverage modern agentic coding tools to increase velocity while maintaining
correctness and readability
Benefits
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
Exciting high traction AI product
