Reinforcement Learning Researcher

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:

Reinforcement Learning

Control systems

Sequential decision-making

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:

Offline RL from historical plant data

Simulation-based training via digital twins

Sim-to-real transfer strategies

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:

Measurable economic impact

Reproducible results

Defensible performance claims