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- Tasks: Lead the development of a pioneering RLOps platform and integrate advanced ML frameworks.
- Company: Fast-growing AI infrastructure startup backed by top-tier VCs.
- Benefits: Flexible work environment, remote policies, and an annual learning budget.
- Why this job: Make a real impact in AI by building cutting-edge tools from the ground up.
- Qualifications: Master’s or Ph.D. in Computer Science or 3+ years in reinforcement learning.
- Other info: Join a dynamic team at the forefront of AI research and innovation.
The predicted salary is between 36000 - 60000 £ per year.
This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers.
Company Description
Fast-growing AI infrastructure startup
Job Description
You will lead the development of a first-of-its-kind RLOps platform, designing scalable infrastructure for RL model training and LLM finetuning. By integrating advanced machine learning frameworks into an open-source ecosystem, you will provide critical tools for businesses to deploy reinforcement learning models effectively while staying at the forefront of AI research.
Location
London, UK
Why this role is remarkable
- Opportunity to build pioneering RLOps infrastructure and open-source tools from the ground up in a high-impact field.
- Join a well-funded venture backed by top-tier VCs at the intersection of reinforcement learning and production-ready MLOps.
- Benefit from a highly flexible work environment with 6-month remote policies and a dedicated annual learning budget.
What you will do
- Design and implement the architecture for a scalable RLOps platform and a robust open-source RL framework.
- Integrate diverse ML libraries and environments to support advanced model training, deployment, and lifecycle management.
- Stay current with the latest RL and MLOps advancements to incorporate cutting‑edge algorithms into the platform’s core.
The ideal candidate
- Holds a Master’s or Ph.D. in Computer Science or has 3+ years of industry experience in reinforcement learning.
- Possesses deep expertise in PyTorch, Ray, or Gym, along with a strong background in hyperparameter optimization.
- Has proven experience building machine learning tooling, cloud‑based distributed infrastructure, and production deployment pipelines.
Next steps
- Visit our website.
- Click 'Talk to Jack'.
- Talk to Jack so he can understand your experience and ambitions.
- Jack will make sure Jill (the AI agent working for the company) considers you for this role.
- If Jill thinks you’re a great fit and her client wants to meet you, they will make the introduction.
- If not, Jack will find you excellent alternatives. All for free.
We never post fake jobs. This isn’t a trick. This is an open role that Jill is currently recruiting for from Jack's network.
Reinforcement Learning Engineer (+ Equity) at fast-growing AI infrastructure startup employer: Jack & Jill/External Ats
Contact Detail:
Jack & Jill/External Ats Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Reinforcement Learning Engineer (+ Equity) at fast-growing AI infrastructure startup
✨Tip Number 1
Get to know Jack! He’s your go-to AI for understanding your skills and career goals. A quick chat with him can help tailor your job search and make sure you’re in the loop for roles like the Reinforcement Learning Engineer position.
✨Tip Number 2
Don’t just wait for jobs to come to you. Actively engage with the community around reinforcement learning and MLOps. Join forums, attend meetups, and connect with others in the field to expand your network and discover hidden opportunities.
✨Tip Number 3
When you apply through our website, make sure to highlight your experience with PyTorch, Ray, or Gym. Tailor your conversation with Jack to showcase how your background aligns with the role’s requirements, especially in building scalable infrastructure.
✨Tip Number 4
Stay updated on the latest advancements in RL and MLOps. Share your insights during your chat with Jack; it shows your passion and commitment to the field, making you a more attractive candidate for cutting-edge roles.
We think you need these skills to ace Reinforcement Learning Engineer (+ Equity) at fast-growing AI infrastructure startup
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with reinforcement learning and any relevant projects you've worked on. We want to see how you can contribute to building that pioneering RLOps infrastructure!
Tailor Your Application: Don’t just send a generic CV! Customise your application to reflect the specific skills and experiences that match the job description. This shows us you’re genuinely interested in the role.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so make it easy for us to see why you’re a great fit for the team without wading through unnecessary fluff.
Apply Through Our Website: Remember to apply through our website! It’s the best way for us to connect you with Jack and ensure your application gets the attention it deserves. Don’t miss out on this opportunity!
How to prepare for a job interview at Jack & Jill/External Ats
✨Know Your Stuff
Make sure you brush up on reinforcement learning concepts, especially around RLOps and the frameworks mentioned like PyTorch, Ray, and Gym. Be ready to discuss your past projects and how they relate to the role.
✨Showcase Your Problem-Solving Skills
Prepare to tackle hypothetical scenarios or case studies during the interview. Think about how you would design scalable infrastructure for RL model training and be ready to explain your thought process clearly.
✨Stay Current with Trends
Familiarise yourself with the latest advancements in RL and MLOps. Mention any recent papers or technologies that excite you, as this shows your passion and commitment to staying at the forefront of AI research.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's vision for their RLOps platform and how they plan to integrate open-source tools. This not only shows your interest but also helps you gauge if the company aligns with your career goals.
