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# Applied AI Scientist, Simulation & Reinforcement Learning

- **Company:** Locus Robotics
- **Location:** United Kingdom (Remote available)
- **Experience:** Expert
- **Salary:** £70,000 - £120,000
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Coverage, Software Quality, Code Review, Programming Tools, Discrete Event Simulation, Python (Programming Language), Machine Learning, Reinforcement Learning, Multi-Agent Systems, Model Validation, Technical Debt, Information Technology, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://startup.jobs/applied-ai-scientist-simulation-reinforcement-learning-locus-robotics-2-10710944

## About the Role

* A PhD in Computer Science, AI/ML, Robotics, Mathematics, Physics, Operations Research or a related quantitative field, or MSc and 5+ years of industry experience applying AI/ML or simulation to real-world problems.
 * Hands-on experience building, extending or validating simulators, ideally discrete-event simulation (SimPy or similar).
 * Practical experience with reinforcement learning and training simulation-based agents, including simulation for RL and Gym-style environments.
 * Strong Python and software engineering fundamentals: testing, code review, maintainable and reproducible code.
 * Rigorous experimental and statistical thinking: benchmarking, tuning, ablations, uncertainty.
 * Experience in robotics, logistics, warehouse or other operations-heavy domains.
 * Publications in major AI, ML, robotics or vision venues (e.g. NeurIPS, ICML, ICLR, AAAI, RSS, CoRL, ICRA, IROS, CVPR, ICCV, ECCV).
 * Excellent English communication skills, both written and verbal, with the ability to engage diverse audiences effectively and explain uncertain results clearly to technical and non-technical audiences in a multidisciplinary environment.

The expected base salary range for this role is GBP 70,000 - 120,000 based on external market data, plus bonus and equity. Actual offers will depend on factors such as the candidate's experience, education, training, key or critical skills, geographic location, and current market and business conditions.

## Description

Own the simulator

 * Own and develop our SimPy-based discrete-event simulator, including its logic, design choices and the fragile areas that need care.
 * Pay down technical debt: improve test coverage, code quality, reproducibility and developer tooling.
 * Build the experiment infrastructure around the simulator: orchestration, study management, reporting and automated runs across benchmark sites.
 * Generate and maintain site data (e.g. distance files, configs) needed to simulate customer warehouses.
 * Support scenario testing and sensitivity analysis for our order-engine and orchestration teams.

Tune and close the sim2real gap

 * Define, measure and reduce the sim-to-real gap against real site data.
 * Run parameter optimisation and tuning studies (e.g. Optuna), and diagnose anomalies and non-determinism.
 * Model warehouse processes such as congestion, picker behaviour and queueing at the right level of fidelity.
 * Communicate results and confidence clearly to engineering, product and operations stakeholders.
 * Use Optimization, RL & DL to tune the simulator

Grow into reinforcement learning

 * Turn the simulator into a proper RL environment (Gymnasium/Gym-style interfaces) with fast, repeatable, scalable training runs.
 * Design, train and evaluate RL policies, starting with guidance generation, and benchmark them against current heuristic algorithms.
 * Guard against overfitting to simulator artefacts, and design experiments that show what transfers to the real world.
 * Prototype, evaluate and productionise learning-based components alongside the wider ML, MLOps and OE teams.
 * Stay current with RL, multi-agent systems and simulation research, and assess what is applicable to Locus.
 * Contribute to internal technical papers, patents and, where appropriate, publications.