[Hiring] Applied AI Scientist @Locus Robotics

Role Description

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.

• Take over day-to-day ownership of our discrete-event simulation (built on SimPy).
• Make the simulation measurably closer to reality.
• Use the simulator as the training and evaluation ground for learning-based policies.
• Compare simulations against live operational data from large fulfilment sites.
• Work in a small, collaborative team where research meets production.

Responsibilities

• Own and develop our SimPy-based discrete-event simulator, including its logic, design choices, and 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.
• 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.

Qualifications

• 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.

Requirements

• Location: This is a remote position based in the United Kingdom. Candidates must reside in the UK.
• Right to Work: Candidates must have the legal right to work in the UK. Locus Robotics is unable to provide visa sponsorship for this position, now or in the future.

Benefits

• 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.