At AgileRL, we are on a mission to democratise access to reinforcement learning for building human-level artificial intelligence systems.

We believe that reinforcement learning will form a part of every sophisticated AI system of the future. It is already having a huge impact on the world we live in, from its use in creating LLMs like ChatGPT to enabling autonomous vehicles to make decisions. Reinforcement learning enables AI models to plan and achieve objectives; however, currently very few companies or individuals have the resources to leverage this powerful machine learning paradigm.

AgileRL is building Arena, an enterprise-grade reinforcement learning operations (RLOps) platform, and a state-of-the-art open-source framework to eliminate these barriers to entry. Our framework has already achieved 10x faster training and hyperparameter optimisation than leading RL libraries. Our Arena platform, built on top of our open-source framework, is focused on four key areas: simulation, training, deployment, and monitoring, with an initial focus on optimising training.

We are already working with companies across industries including finance, defence, technology, transport, and sport, and are looking for talented engineers to join the team and develop the systems and tools that will enable the next wave of impactful AI.

As a member of the AgileRL team, you will have the opportunity to be at the forefront of reinforcement learning innovation. We value curiosity, creativity, and a passion for pushing boundaries. Together, we will build not only state-of-the-art software but also a culture of excellence, collaboration, and continuous learning.

We are seeking a talented and versatile Full Stack Engineer to join our team at AgileRL and contribute to the further development of Arena, a web-based software platform for reinforcement learning training and RLOps.

As a Full Stack Engineer, you will play a key role in designing, implementing, and maintaining the frontend and backend components of Arena, enabling businesses to build and operationalise reinforcement learning models effectively.

Responsibilities:

• Collaborate with the team to understand requirements, and design and build features for the Arena platform.
• Design and implement robust and scalable backend systems, APIs, and services to support machine learning model training, deployment, and management.
• Integrate and interact with reinforcement learning frameworks and libraries, ensuring seamless interoperability between frontend and backend components.
• Establish pipelines for automated build, testing, and deployment within the Arena platform.
• Stay up-to-date with the latest advancements in software development, MLOps, reinforcement learning, and best practices, incorporating them into the platform as appropriate.
• Provide technical guidance and support to internal users and external customers using the Arena platform.

Requirements:

• Bachelor's or higher degree in Computer Science, Engineering, or a related field.
• Experience in developing data-intensive applications or working with MLOps tools.
• Proven experience as a Full Stack Engineer, with a focus on machine learning/AI applications.
• Familiarity with containerisation technologies (e.g. Docker) and orchestration tools (e.g. Kubernetes).
• Proficiency in modern frontend frameworks and technologies such as React, Vue.js, or AngularJS.
• Experience in designing and implementing RESTful APIs and backend systems using frameworks like Node.js, Django, Rust, or Flask.
• Familiarity with databases and data management techniques for handling large-scale datasets.
• Experience with cloud service providers such as AWS, GCP, or Azure.
• Deep understanding of software engineering, security, and DevOps principles and best practices.
• Strong problem-solving and communication skills, and the ability to work independently as well as in a team environment.

Compensation:

• Competitive salary + significant stock options.
• 30 days of holiday, plus bank holidays, per year.
• Flexible working from home and 6-month remote working policies.
• Enhanced parental leave.
• Learning budget of £500 per calendar year for books, training courses, and conferences.
• Company pension scheme.
• Regular team socials and quarterly all-company parties.
• Bike2Work scheme.

Join the fast-growing AgileRL team and play a key role in the development of cutting-edge reinforcement learning tooling and infrastructure.