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- Tasks: Design and develop cutting-edge reinforcement learning models and infrastructure.
- Company: Join a fast-growing tech team focused on innovative AI solutions.
- Benefits: Competitive salary, stock options, 30 days holiday, and flexible remote work.
- Why this job: Make a real impact in the AI field with exciting projects and a collaborative culture.
- Qualifications: Master's/Ph.D. in Computer Science or 3+ years of relevant experience required.
- Other info: Enjoy a dynamic work environment with regular team socials and a learning budget.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking a talented and experienced Machine Learning Engineer to join our team. This engineer will contribute to the further development of Arena, a web-based software platform for LLM- 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 LLM training, reinforcement fine-tuning, 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 LLM training, reinforcement learning algorithms and concepts, with hands-on experience in building and training AI models.
- Strong programming skills, with experience using ML frameworks and libraries (e.g. PyTorch, TensorFlow, Ray, Gym, TRL, DeepSpeed, VLLM), and MLOps tools.
- 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.
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 (LLMs) employer: AgileRL Ltd
Contact Detail:
AgileRL Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (LLMs)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to LLMs and reinforcement learning. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and algorithms. Practice explaining your past projects clearly and confidently, as communication is key in tech roles.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love hearing from passionate candidates like you!
We think you need these skills to ace Machine Learning Engineer (LLMs)
Some tips for your application 🫡
Show Your Passion: When you're writing your application, let your enthusiasm for machine learning and reinforcement learning shine through. We want to see what excites you about this field and how you can contribute to our mission at StudySmarter.
Be Specific: For the longer-form questions, don’t just give us general answers. Dive into specifics about your experiences and ideas. We’re looking for concrete examples of your work with LLMs and RL algorithms that demonstrate your expertise and creativity.
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the job description. We want to see your programming skills and any hands-on experience with ML frameworks. Tailoring your CV shows us you’re serious about the role!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way to ensure your application gets to us directly. Plus, it gives you a chance to review everything before hitting send, so you can make sure it’s just right.
How to prepare for a job interview at AgileRL Ltd
✨Know Your Stuff
Make sure you brush up on your knowledge of large language models and reinforcement learning algorithms. Be ready to discuss specific frameworks like PyTorch or TensorFlow, and have examples of your past work handy. This shows you’re not just familiar with the theory but have practical experience too.
✨Showcase Your Problem-Solving Skills
Prepare to talk about challenges you've faced in previous projects, especially those related to model deployment and management. Highlight how you approached these problems and what solutions you implemented. This will demonstrate your critical thinking and adaptability.
✨Engage with the Team
Since collaboration is key in this role, be ready to discuss how you’ve worked with teams in the past. Share examples of how you’ve contributed to feature design or provided technical support. This will show that you can communicate effectively and work well in a team environment.
✨Stay Current
Keep yourself updated on the latest advancements in AI and MLOps. Mention any recent trends or tools you’ve been following and how they could benefit the Arena platform. This shows your passion for the field and your commitment to continuous learning.