We are seeking a talented and experienced Machine Learning Engineer with a background in Reinforcement Learning to join our team. This engineer will contribute to the further development of Arena, a web-based software platform for reinforcement learning training and RLOps, and our open-source reinforcement learning library.
As a Machine Learning Engineer, you will be responsible for designing, implementing, and maintaining the infrastructure, tools, and services that enable businesses to build and deploy reinforcement learning models efficiently and effectively.
Responsibilities
- Collaborate with the team to understand requirements and design new features of the Arena platform and open-source framework.
- Develop scalable and reliable infrastructure to support reinforcement learning model training, LLM finetuning, model deployment, and management.
- Integrate existing machine learning frameworks and libraries into the platform and open-source framework, providing a range of algorithms, environments, and tools for reinforcement learning model development.
- Stay up-to-date with the latest advancements in AI, MLOps, reinforcement learning algorithms, tools, and techniques, and incorporate them into the platform as appropriate.
Provide technical guidance and support to internal users and external customers using the Arena platform and open-source framework.
Requirements
- Master's or Ph.D. degree in Computer Science, Engineering, or a related field, or 3+ years of relevant industry experience.
- Solid understanding of reinforcement learning algorithms and concepts, with hands-on experience in building and training reinforcement learning models.
- Strong programming skills, with experience using reinforcement learning and ML frameworks and libraries (e.g. PyTorch, TensorFlow, Ray, Gym, RLLib, SB3, TRL), and MLOps tools.
- Solid understanding of hyperparameter optimisation techniques and strategies.
- Experience in building machine learning platforms or tooling for industrial or enterprise settings.
- Proficiency in data management techniques, including storage, retrieval, and pre-processing of large-scale datasets.
- Familiarity with model deployment and management, including the development of APIs, deployment pipelines, and performance optimisation.
- Experience in 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, 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.
