At a Glance
- Tasks: Join our reinforcement learning team to enhance AI reasoning and coding skills.
- Company: Poolside, a pioneering company shaping AI for human-level intelligence.
- Benefits: Enjoy remote work, flexible hours, 37 days off, and health insurance.
- Other info: Be part of a diverse, inclusive culture with excellent career growth opportunities.
- Why this job: Make a real impact on AI development with cutting-edge technology and thousands of GPUs.
- Qualifications: Experience with Large Language Models and strong deep learning fundamentals required.
The predicted salary is between 36000 - 60000 £ per year.
ABOUT POOLSIDE
In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.
Poolside exists to be this company: to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises.
ABOUT OUR TEAM
We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.
Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development.
ABOUT THE ROLE
You would be working on our reinforcement learning team focused on improving reasoning and coding abilities of Large Language Models through reinforcement learning. This is a hands-on role where you’ll work end-to-end from researching new exploration or training algorithms, to designing and scaling up RL environments, to implementing your ideas across the stack. You will have access to thousands of GPUs in this team.
YOUR MISSION
To push the frontier of reasoning and coding capabilities of foundational models, via Reinforcement Learning.
RESPONSIBILITIES
Research and experiment on ways to improve reasoning and code generation for LLMs. Own the full experiment life cycle from idea to experimentation and integration
Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation. Translate research ideas into clean, reusable codebases that other researchers can build on
Design, analyze, and iterate on data generation and training of LLMs
Implement and iterate on RL training pipelines that scale reliably across domains
Diagnose training instabilities and failures, debug RL runs and propose mitigation methods
Write high-quality, reproducible and maintainable code
SKILLS & EXPERIENCE
Experience with Large Language Models (LLM), including:
Understanding of the Transformer architecture and scaling laws
Mid-training and post-training techniques
Experience training reasoning and/or agentic models
Hands-on use of LLMs, with a sense of their capabilities and limitations
Reinforcement Learning experience
Solid grasp of Reinforcement Learning concepts and familiarity with modern algorithms
Experience developing distributed, large-scale RL pipelines from data creation to evaluations
Research experience
Scientific publications in any of the following topics: Reinforcement Learning, LLMs and reasoning models
Ability to discuss the latest research with sufficient level of detail
Is reasonably opinionated
Engineering skills
Strong machine learning, algorithm skills and engineering background
Experience with distributed training
Excellent programming skills in Python
Familiarity with a deep learning framework (Pytorch or JAX)
PROCESS
Intro call with one of our Founding Engineers
Technical Interview(s) with one of our Founding Engineers
Team fit call with the People team
Final interview with one of our Founding Engineers
BENEFITS
Fully remote work & flexible hours
37 days/year of vacation & holidays
Health insurance allowance for you & dependents
16 weeks of flexible, full-pay parental leave
Well-being, always-be-learning & home office allowances
Company-provided equipment
Frequent team get togethers
Diverse & inclusive people-first culture
Member of Engineering (Reinforcement Learning) employer: poolside
At Poolside, we are committed to fostering a dynamic and inclusive work environment that empowers our team members to thrive. With fully remote work options, generous vacation policies, and a strong focus on employee well-being, we ensure that our engineers can balance their professional ambitions with personal growth. Our unique culture, characterised by low ego and kindness, combined with regular team gatherings in vibrant locations like Paris, makes Poolside an exceptional place for those looking to make a meaningful impact in the world of AI and software development.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Engineering (Reinforcement Learning)
✨Tip Number 1
Network like a pro! Reach out to folks in the reinforcement learning space on LinkedIn or Twitter. Join relevant groups and forums where you can share ideas and learn from others. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLMs and reinforcement learning. Whether it's GitHub repos or personal blogs, let your work speak for itself. This is your chance to shine!
✨Tip Number 3
Prepare for those interviews! Brush up on your deep learning fundamentals and be ready to discuss your experience with LLMs. Practice coding challenges and be prepared to explain your thought process. Confidence is key!
✨Tip Number 4
Apply through our website! We love seeing passionate candidates who are eager to join our team. Make sure to tailor your application to highlight your relevant experience and enthusiasm for AI and reinforcement learning.
We think you need these skills to ace Member of Engineering (Reinforcement Learning)
Some tips for your application 🫡
Show Your Passion for AI:When you're writing your application, let your enthusiasm for AI and reinforcement learning shine through. We want to see that you’re genuinely excited about pushing the boundaries of Large Language Models and how they can evolve.
Tailor Your Experience:Make sure to highlight your relevant experience with LLMs and reinforcement learning in your application. We’re looking for specific examples of your work, so don’t hold back on sharing those projects that showcase your skills!
Keep It Clear and Concise:While we love detail, clarity is key! Make your application easy to read by keeping your language straightforward and your points concise. This helps us quickly grasp your qualifications and fit for the role.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at poolside
✨Know Your Stuff
Make sure you brush up on your knowledge of Large Language Models and reinforcement learning. Familiarise yourself with the latest research and be ready to discuss how you've applied these concepts in your previous work. This will show that you're not just a theoretical thinker but someone who can implement ideas practically.
✨Show Off Your Coding Skills
Since this role involves writing high-quality code, be prepared to demonstrate your programming abilities. Bring examples of your work, especially in Python with PyTorch or Jax. You might even want to do a quick coding exercise during the interview, so practice common algorithms and coding challenges beforehand.
✨Be Ready to Discuss Your Experiments
You’ll need to own the full experiment life cycle, so come prepared to talk about your past projects. Discuss the experiments you’ve designed, the results you’ve achieved, and any challenges you faced. This will highlight your hands-on experience and problem-solving skills.
✨Cultural Fit Matters
Poolside values a diverse and inclusive culture, so be yourself! Show your enthusiasm for working in a remote-first team and how you can contribute to their collaborative environment. Share your thoughts on teamwork and how you align with their mission to push the boundaries of AI.
