Applied Scientist, Machine Learning (Reinforcement Learning)

Applied Scientist, Machine Learning (Reinforcement Learning)

London | Hybrid (1-2 days per week in office) | £100-110k + Equity

About the Company

An ambitious, well-funded technology scale-up developing advanced AI solutions for complex real-world challenges. Following recent investment, the business is expanding its technical team to accelerate the development of innovative machine learning technologies.

This is an opportunity to join a growing, highly technical team working at the intersection of AI research, engineering and real-world applications.

The Role

We are seeking an Applied Scientist specialising in Reinforcement Learning to develop intelligent models capable of learning, adapting and making decisions within complex environments.

You will work across the full ML lifecycle, from research and experimentation through to model training, evaluation and deployment, collaborating with engineers and domain specialists to bring advanced AI solutions into practical applications.

Key Responsibilities

• Design, train and evaluate deep reinforcement learning agents.
• Translate complex real-world challenges into effective ML problems, including state/action design, reward engineering and constraints.
• Develop and refine training methodologies, models and simulation environments.
• Evaluate model behaviour and ensure solutions are robust, reliable and grounded in real-world principles.
• Collaborate with software engineers to integrate and deploy ML models into production environments.
• Contribute to research, experimentation and ongoing technical development.

Essential Experience

• Degree in Engineering, Physics, Mathematics or a relevant quantitative discipline.
• Strong practical experience with Reinforcement Learning and training/debugging deep RL agents.
• Proficiency in Python and ML frameworks such as PyTorch or JAX.
• Experience working with time-series data, dynamic systems or complex datasets.
• Strong analytical and problem-solving skills with the ability to translate ambiguous challenges into ML solutions.
• Comfortable working across both research and practical engineering.

Desirable Experience

• Robotics, autonomous systems, control engineering or industrial optimisation.
• Control theory, MPC or optimal control.
• Simulation, digital twins or physics-based modelling.
• Offline RL, safe RL, meta-learning or multi-task learning.
• Distributed training, federated learning or edge deployment.
• Relevant MSc/PhD, publications or applied research experience.

Why Join?

• Work on innovative AI with meaningful real-world impact.
• Join a well-funded, rapidly growing technology business.
• Collaborate with talented scientists and engineers.
• Enjoy significant technical ownership and influence over research direction.
• Help bring cutting-edge AI research into practical applications.

Interested in exploring a challenging opportunity at the forefront of applied AI? I'd love to have a conversation