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# Reinforcement Learning Researcher

- **Company:** Applied Computing
- **Location:** London, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Reinforcement Learning, Digital Twin, Pytorch, Delivery Pipeline, Deep Learning, Machine Learning Operations, Dynamic Programming, Docker
- **Published:** August 6, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=5b3e6023196b622a

## About the Role

* 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

## Description

* 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