Machine Learning Engineer (Reinforcement Learning)

I am recruiting on behalf of a leading client in the technology sector who is seeking a highly skilled and experienced Machine Learning Engineer with a strong background in Reinforcement Learning. This role will contribute to the continued development of Arena, the company's web-based platform for reinforcement learning training and RLOps, as well as its open-source reinforcement learning library.
In this position, you will be responsible for designing, implementing and maintaining the infrastructure, tools and services that enable organisations to build and deploy reinforcement learning models efficiently and at scale.

Responsibilities

Work closely with the team to understand requirements and design new features for both the Arena platform and the open-source framework.

Develop scalable and reliable infrastructure to support reinforcement learning model training, LLM fine-tuning, model deployment, and ongoing management.

Integrate existing machine learning frameworks and libraries into the platform and open-source tools, ensuring a broad range of algorithms, environments, and utilities for reinforcement learning development.

Keep abreast of the latest advancements in AI, MLOps, reinforcement learning algorithms, tools and techniques, and incorporate relevant developments into the platform.

Provide technical guidance and support to internal users and external customers working with the Arena platform and associated open-source tools.

Requirements

Master's or PhD in Computer Science, Engineering, or a related field, or at least 3 years of relevant industry experience.

Strong understanding of reinforcement learning algorithms and concepts, with practical experience in building and training reinforcement learning models.

Excellent programming skills, with experience using reinforcement learning and ML frameworks (e.g. PyTorch, TensorFlow, Ray, Gym, RLLib, SB3, TRL) and MLOps tools.

Solid understanding of hyperparameter optimisation techniques and strategies.

Experience building machine learning platforms or tooling for industrial or enterprise environments.

Proficiency in data management techniques, including the storage, retrieval, and preprocessing of large-scale datasets.

Familiarity with model deployment and management, including API development, deployment pipelines, and performance optimisation.

Experience designing and developing cloud-based infrastructure for distributed computing and scalable data processing.

Deep understanding of software engineering and machine learning principles and best practices.

Strong problem-solving and communication skills, with the ability to work both independently and collaboratively.

Compensation and Benefits

30 days' holiday per year, plus bank holidays.

Flexible working from home and a 6-month remote working policy.

Enhanced parental leave.

£500 annual learning budget for books, training courses, and conferences.

Company pension scheme.

Regular team socials and quarterly company-wide events.

Bike2Work scheme.

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