We are seeking a talented and experienced Front end Engineer 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. Responsibilities Develop scalable and reliable infrastructure to support a reinforcement learning model training, deployment, and management platform. Collaborate with the team to understand requirements, and design and develop the user facing interface of the platform. Work closely with designers and engineers to implement the visual design elements within brand guidelines. Ensure that the platform works consistently across various web browsers and devices, including testing and resolving compatibility issues. Optimize the platform for speed and efficiency, minimizing load times and ensuring a smooth user experience. Ensure that web content is accessible to all users by adhering to Accessibility standards like Web Content Accessibility Guidelines. Work closely with users to identify and rectify user facing bugs and issues to constantly improve usability of the platform. Keep thorough documentation of code and development processes to help maintain and troubleshoot the platform. Requirements Bachelor's degree or higher in Computer Science, Engineering, or a related field, or 3+ years of relevant industry experience. Solid understanding of HTML, CSS (Tailwind) and JavaScript/TypeScript, with hands on experience building platforms. Proficient with front end frameworks and libraries like ReactJS, Redux, GraphQL to streamline development and enhance user interactivity. Experience with integrating with various APIs (REST API or GraphQL using one of fetch, Redux, Relay, GraphQL) to fetch and display data from external sources. Familiarity with tools for monitoring and improving platform page performance metrics, including page load times and resource optimisation. Deep understanding of software engineering 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.
03/02/2026
Full time
We are seeking a talented and experienced Front end Engineer 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. Responsibilities Develop scalable and reliable infrastructure to support a reinforcement learning model training, deployment, and management platform. Collaborate with the team to understand requirements, and design and develop the user facing interface of the platform. Work closely with designers and engineers to implement the visual design elements within brand guidelines. Ensure that the platform works consistently across various web browsers and devices, including testing and resolving compatibility issues. Optimize the platform for speed and efficiency, minimizing load times and ensuring a smooth user experience. Ensure that web content is accessible to all users by adhering to Accessibility standards like Web Content Accessibility Guidelines. Work closely with users to identify and rectify user facing bugs and issues to constantly improve usability of the platform. Keep thorough documentation of code and development processes to help maintain and troubleshoot the platform. Requirements Bachelor's degree or higher in Computer Science, Engineering, or a related field, or 3+ years of relevant industry experience. Solid understanding of HTML, CSS (Tailwind) and JavaScript/TypeScript, with hands on experience building platforms. Proficient with front end frameworks and libraries like ReactJS, Redux, GraphQL to streamline development and enhance user interactivity. Experience with integrating with various APIs (REST API or GraphQL using one of fetch, Redux, Relay, GraphQL) to fetch and display data from external sources. Familiarity with tools for monitoring and improving platform page performance metrics, including page load times and resource optimisation. Deep understanding of software engineering 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.
We are seeking a talented and experienced DevOps Engineer 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. As a DevOps Engineer, you will be responsible for designing, implementing, and maintaining the cloud infrastructure, CI/CD pipelines, and deployment systems that enable businesses to build and deploy reinforcement learning models at scale. Responsibilities Design and maintain robust, scalable cloud infrastructure to support high-performance reinforcement learning workloads and distributed training environments Build and optimise CI/CD pipelines for both our open-source framework and Arena enterprise platform, ensuring reliable deployments and automated testing Implement and manage containerisation strategies using Docker and Kubernetes for ML model training, deployment, and orchestration Develop infrastructure as code (IaC) solutions using tools like Terraform, CloudFormation, or Pulumi to ensure reproducible and version-controlled infrastructure Monitor system performance, implement alerting and logging solutions, and troubleshoot production issues across distributed ML training environments Collaborate with ML engineers to optimise resource allocation and cost efficiency for compute-intensive RL training workloads Implement security best practices, manage access controls, and ensure compliance with enterprise security requirements Automate operational tasks including backup strategies, disaster recovery procedures, and system maintenance Support the deployment and scaling of GPU clusters and distributed computing resources for reinforcement learning applications Maintain high availability and performance of production systems serving ML models to external customers Requirements Bachelor's degree or higher in Computer Science, Engineering, or a related field, or 3+ years of relevant DevOps/infrastructure experience Strong experience with cloud platforms (AWS, GCP, Azure) and their ML/AI services, with expertise in managing compute-intensive workloads Proficiency in containerisation technologies (Docker, Kubernetes) and container orchestration for ML workloads Experience with Infrastructure as Code tools (Terraform, CloudFormation, Pulumi) and configuration management Solid understanding of CI/CD principles and tools (GitHub Actions, GitLab CI, Jenkins) with experience in ML pipeline automation Knowledge of monitoring and observability tools (Prometheus, Grafana, OpenObserve) and their application to ML systems Experience with GPU infrastructure management and distributed computing frameworks for machine learning Familiarity with MLOps practices and tools for model deployment, versioning, and lifecycle management Strong scripting skills in Python, Bash, or similar languages for automation tasks Understanding of networking, security, and database management in cloud environments Experience with high-performance computing environments and job scheduling systems is a plus Knowledge of machine learning workflows and the unique infrastructure requirements of ML training and inference Strong problem-solving skills and ability to work in a fast-paced, collaborative environment Excellent communication skills and experience working with cross-functional teams 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. Note: for the following longer form questions we have received an overwhelming number of applications with answers that are AI generated. Any application that uses AI generated answers will not be considered. What motivates you to apply to this role, and what are you looking forward to in contributing towards the AgileRL mission? (200 words max) What unique experience do you have with building and managing infrastructure for machine learning or data intensive platforms that makes you the ideal candidate for this role? (200 words max) What three infrastructure or operational improvements you believe would be most valuable for scaling a reinforcement learning platform like Arena, and how would you implement them? (200 words max) Application details: Full name Email address LinkedIn profile Country of residence Availability to start Upload your CV Upload File (Max size 10 MB) I agree to the Privacy Policy
03/02/2026
Full time
We are seeking a talented and experienced DevOps Engineer 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. As a DevOps Engineer, you will be responsible for designing, implementing, and maintaining the cloud infrastructure, CI/CD pipelines, and deployment systems that enable businesses to build and deploy reinforcement learning models at scale. Responsibilities Design and maintain robust, scalable cloud infrastructure to support high-performance reinforcement learning workloads and distributed training environments Build and optimise CI/CD pipelines for both our open-source framework and Arena enterprise platform, ensuring reliable deployments and automated testing Implement and manage containerisation strategies using Docker and Kubernetes for ML model training, deployment, and orchestration Develop infrastructure as code (IaC) solutions using tools like Terraform, CloudFormation, or Pulumi to ensure reproducible and version-controlled infrastructure Monitor system performance, implement alerting and logging solutions, and troubleshoot production issues across distributed ML training environments Collaborate with ML engineers to optimise resource allocation and cost efficiency for compute-intensive RL training workloads Implement security best practices, manage access controls, and ensure compliance with enterprise security requirements Automate operational tasks including backup strategies, disaster recovery procedures, and system maintenance Support the deployment and scaling of GPU clusters and distributed computing resources for reinforcement learning applications Maintain high availability and performance of production systems serving ML models to external customers Requirements Bachelor's degree or higher in Computer Science, Engineering, or a related field, or 3+ years of relevant DevOps/infrastructure experience Strong experience with cloud platforms (AWS, GCP, Azure) and their ML/AI services, with expertise in managing compute-intensive workloads Proficiency in containerisation technologies (Docker, Kubernetes) and container orchestration for ML workloads Experience with Infrastructure as Code tools (Terraform, CloudFormation, Pulumi) and configuration management Solid understanding of CI/CD principles and tools (GitHub Actions, GitLab CI, Jenkins) with experience in ML pipeline automation Knowledge of monitoring and observability tools (Prometheus, Grafana, OpenObserve) and their application to ML systems Experience with GPU infrastructure management and distributed computing frameworks for machine learning Familiarity with MLOps practices and tools for model deployment, versioning, and lifecycle management Strong scripting skills in Python, Bash, or similar languages for automation tasks Understanding of networking, security, and database management in cloud environments Experience with high-performance computing environments and job scheduling systems is a plus Knowledge of machine learning workflows and the unique infrastructure requirements of ML training and inference Strong problem-solving skills and ability to work in a fast-paced, collaborative environment Excellent communication skills and experience working with cross-functional teams 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. Note: for the following longer form questions we have received an overwhelming number of applications with answers that are AI generated. Any application that uses AI generated answers will not be considered. What motivates you to apply to this role, and what are you looking forward to in contributing towards the AgileRL mission? (200 words max) What unique experience do you have with building and managing infrastructure for machine learning or data intensive platforms that makes you the ideal candidate for this role? (200 words max) What three infrastructure or operational improvements you believe would be most valuable for scaling a reinforcement learning platform like Arena, and how would you implement them? (200 words max) Application details: Full name Email address LinkedIn profile Country of residence Availability to start Upload your CV Upload File (Max size 10 MB) I agree to the Privacy Policy
Machine Learning Engineer (Reinforcement Learning) 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. 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. Apply below
02/02/2026
Full time
Machine Learning Engineer (Reinforcement Learning) 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. 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. Apply below