Applied AI Scientist, Simulation & Reinforcement Learning

At Locus Robotics, we build AI-powered systems and intelligent robots that keep global supply chains running. Our platform combines advanced AI, real-time decision-making, and autonomous robotics to help leading companies improve efficiency, scale operations, and adapt to constant change.

Locus Robotics is a place to do meaningful work with real-world impact, where your ideas move quickly from concept to deployment. We invest in our people, encourage continuous learning, and give you the opportunity to grow your career while building technology that is used every day at global scale.

Own the simulator

• Own and develop our Sim Py-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.

• 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

• 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 product ionise 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.

The expected base salary range for this role is GBP 70, 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.

As an Applied AI Scientist, Simulation & Reinforcement Learning, you'll own the simulator behind SDL, our programme building a world-first, reinforcement-learning-driven orchestration framework for people and robots. That framework needs a simulator that is trustworthy enough to develop, tune and validate it before it touches a live warehouse. You will take over day-to-day ownership of our discrete-event simulation (built on Sim Py), make it measurably closer to reality, and use it as the training and evaluation ground for learning-based policies.

Simulation comes first; reinforcement learning grows to be the larger part of the role as the simulator matures. You'll compare your simulations against live operational data from some of the world's largest fulfilment sites, and your work will decide what is validated and shipped to fleets of robots in customer sites worldwide. This is not a notebook role. The simulator you own is the foundation that decides what ships to customer sites worldwide.

You will work in a small, collaborative team where research meets production, as part of Applied…