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
What You’ll Own
Orbital’s learning-based optimisation and control stack
RL + control hybrid systems for industrial processes
Safe and constrained policy learning frameworks
Simulation environments and digital twin integrations
Research production translation for RL systems
Benchmarking standards for decision-making systems
Must-Have Qualifications
PhD in Computer Science, Robotics, Control, Applied Mathematics, or related field
First-author publications in:
3+ years of hands-on RL research experience
Strong foundation in:
Reinforcement Learning (online + offline)
Optimisation and control theory (MPC, dynamic programming, etc.)
Deep learning (PyTorch)
Experience with:
Real-world deployment of ML systems
Simulation environments or digital twins
Working with noisy, real-world data
How We Work
Research is judged by production impact, not paper count
We optimise for real systems, not benchmarks alone
We value safe, reliable decision-making over theoretical elegance
Physics, control, and learning are treated asone system
What This Role Is Not
Not toy RL environments (Atari,MuJoCo-only thinking)
Not unconstrainedpolicy learning without safety guarantees
Not offline research disconnected from deployment
Not a support role; this position owns core optimisation IP
Core Responsibilities
1. Design & Implement RL-Based Decision Systems
Process optimisation (yield, efficiency, cost reduction)
Control policy learning (setpoint optimisation, constraint handling)
Sequential decision-making under uncertainty
Work across:
Model-free RL (policy gradients, actor-critic, offline RL)
Model-based RL (world models, planning-based methods)
Hybrid approaches combining RL with optimisation / MPC
2. Build Physics-Constrained RL Systems
Embed domain knowledge into policy learning:
Hard constraints (safety, operating limits, regulatory bounds)
Soft constraints (efficiency, degradation, economic trade-offs)
Physics-informed reward shaping and transition models
Ensure policies:
Respect physical feasibility
Generalise across operating regimes
Remain stable under real-world disturbances
3. Offline RL, Simulation & Digital Twin Integration
Develop RL systems that work in data-scarce and risk-sensitive environments:
Handle:
Distribution shift
Partial observability
Sparse / delayed rewards
4. Safety, Robustness & Interpretability
Design safe RL systems for production environments:
Constrained RL / safe exploration
Policy validation before deployment
Fail-safe mechanisms and fallback strategies
Ensure outputs are:
Interpretable to engineers and operators
Auditable and explainable
Reliable under sensor faults and regime changes
5. Production-Grade Deployment
Deploy RL systems into real-world infrastructure:
Containerised deployment (Docker, AWS / Azure)
Integration with control systems (APC, DCS, advisory layers)
Real-time inference and monitoring
Build pipelines for:
Continuous policy evaluation
Safe rollout and rollback
Online / batch policy updates
6. Benchmarking & Validation
Define evaluation standards for RL systems:
Offline policy evaluation
Counterfactual analysis
Comparison vs MPC, heuristics, and operator baselines
Ensure:
