At a Glance
- Tasks: Build and scale high-performance data pipelines for AI model training.
- Company: Join a pioneering company at the forefront of Artificial General Intelligence.
- Benefits: Enjoy fully remote work, flexible hours, and 37 days of vacation.
- Other info: Collaborative team culture with opportunities for personal and professional growth.
- Why this job: Make a real impact in AI by delivering high-quality datasets for innovative models.
- Qualifications: Experience in distributed data systems and strong Python skills 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.
ABOUT OUR TEAM
We are a remote-first team that sits across Europe and North America. We come together once a month in-person 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 combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts.
ABOUT THE ROLE
You will be a core member of our Pretraining Data team, responsible for building and scaling our Model Factory: our system for quickly training, scaling, and experimenting with our foundation models. This is a hands-on role where your #1 mission is to architect and maintain the high-performance pipelines that transform trillions of raw tokens into the high-quality dataset "fuel" our models require. To enable us to conduct and implement latest research, you’ll be engineering the ingestion, deduplication, and streaming systems that handle petabyte-scale data. You will bridge the gap between raw web crawls and our GPU clusters, directly influencing model performance through superior data modeling, algorithmic sorting, and distributed pipeline optimization. You will be closely collaborating with other teams like Pretraining, Posttraining, Evals, and Product to generate high-quality datasets that map to missing model capabilities and downstream use cases.
YOUR MISSION
To deliver large, high-quality, and diverse datasets of natural language and source code for training poolside models and coding agents.
RESPONSIBILITIES
- Build and maintain high-performance pipelines for trillions of tokens.
- Deliver diverse and high-quality datasets for pre-training foundation models.
- Closely work with other teams such as Pretraining, Posttraining, Evals and Product to ensure alignment on the quality of the models delivered.
SKILLS & EXPERIENCE
- Strong background in building production-grade, distributed data systems for machine learning, with experience in:
- Orchestration: Slurm, Airflow, or Dagster
- Observability & Reliability: CI/CD, Grafana, Prometheus, etc.
- Infra: Git, Docker, k8s, cloud managed services
- Batched inference (ex: vLLM)
- Performance obsession, especially with large-scale GPU clusters and distributed pipelines
- Expert-level python knowledge and ability to write clean and maintainable code
- Strong algorithmic foundations
- Proficiency with libraries like Polars, Dask, or PySpark
- Nice to have:
- Experience in building trillion-scale SOTA pretraining datasets
- Experience translating research to production at scale
- Experience with OCR, web crawling, or evals
- Prior experience pre-training LLMs
PROCESS
- Intro call with Eiso, our CTO & Co-Founder
- 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 and dependents
- Company-provided equipment
- Wellbeing, always-be-learning and home office allowances
- Frequent team get-togethers
- Great diverse & inclusive people-first culture
Member of Engineering (Pre-training / Data Engineering) 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 (Pre-training / Data Engineering)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already at Poolside. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got a project or two that showcases your data engineering prowess, share them. Whether it's on GitHub or a personal website, let your work speak for itself.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss how you can contribute to building high-performance pipelines. Practice makes perfect!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in being part of the Poolside team.
We think you need these skills to ace Member of Engineering (Pre-training / Data Engineering)
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the role. Highlight your experience with data systems and any relevant projects that showcase your skills in building high-performance pipelines. We want to see how you can contribute to our mission!
Showcase Your Technical Skills:Don’t hold back on your technical prowess! Mention specific tools and technologies you've worked with, like Slurm, Airflow, or Python libraries. We love seeing candidates who are passionate about performance and reliability in their work.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points where possible to make it easy for us to read through your achievements and experiences. We appreciate a well-structured application that gets straight to the good stuff!
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 shows you’re keen on joining our team at Poolside!
How to prepare for a job interview at poolside
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like orchestration tools and distributed data systems. Brush up on your Python skills and be ready to discuss how you've used libraries like Polars or Dask in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in building high-performance pipelines or handling large datasets. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your problem-solving abilities.
✨Understand the Company’s Vision
Dive deep into Poolside's mission of creating Artificial General Intelligence. Be ready to articulate how your role as a Member of Engineering aligns with their goals and how you can contribute to building the Model Factory.
✨Be Ready for Collaboration Questions
Since the role involves working closely with various teams, prepare examples of how you've successfully collaborated in the past. Think about times when you aligned with others on project goals or tackled cross-team challenges.
