At a Glance
- Tasks: Develop and enhance a web-based platform for reinforcement learning and contribute to open-source projects.
- Company: Join the fast-growing AgileRL team focused on cutting-edge AI technologies.
- Benefits: Competitive salary, stock options, 30 days holiday, flexible working, and a £500 learning budget.
- Why this job: Make a real impact in AI while working with innovative tools and technologies.
- Qualifications: Master’s/Ph.D. in Computer Science or 3+ years of relevant experience in ML.
- Other info: Enjoy regular team socials and excellent career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
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.
Machine Learning Engineer (Reinforcement Learning) London, UK in City of London employer: AgileRL Ltd
Contact Detail:
AgileRL Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (Reinforcement Learning) London, UK in City of London
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or conferences related to machine learning and reinforcement learning. You never know who might be looking for someone just like you!
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those involving reinforcement learning. Share your code on GitHub and write about your experiences on platforms like LinkedIn. This will help us see your practical skills in action!
✨Ace the Interview
Prepare for technical interviews by brushing up on reinforcement learning concepts and algorithms. Practice coding challenges and be ready to discuss your past projects. Confidence is key, so show us what you've got!
✨Apply Through Our Website
Don't forget to apply directly through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Machine Learning Engineer (Reinforcement Learning) London, UK in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with reinforcement learning and relevant programming skills. We want to see how your background aligns with the role, so don’t be shy about showcasing your projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about reinforcement learning and how you can contribute to our Arena platform. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Projects: If you've worked on any cool projects related to machine learning or reinforcement learning, make sure to mention them! We’re keen to see practical examples of your work, especially if they involve the tools and frameworks we use.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at AgileRL Ltd
✨Know Your Algorithms
Brush up on your reinforcement learning algorithms and concepts. Be ready to discuss specific models you've built or trained, and how you approached challenges in those projects. This shows your hands-on experience and deep understanding of the field.
✨Showcase Your Programming Skills
Prepare to demonstrate your programming prowess, especially with frameworks like PyTorch or TensorFlow. You might be asked to solve a coding problem on the spot, so practice common ML tasks and be familiar with the libraries mentioned in the job description.
✨Discuss Infrastructure Experience
Be ready to talk about your experience with building scalable infrastructure for model training and deployment. Share examples of how you've integrated existing ML frameworks into platforms, and any MLOps tools you've used to streamline processes.
✨Stay Current with Trends
Make sure you're up-to-date with the latest advancements in AI and reinforcement learning. Mention any recent papers or tools that have caught your attention, and be prepared to discuss how you would incorporate new techniques into the Arena platform.
