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- Tasks: Join our data team to enhance dataset quality for training AI models.
- Company: Poolside, a pioneering company in Artificial General Intelligence.
- Benefits: Fully remote work, flexible hours, 37 days of vacation, and health insurance.
- Why this job: Be at the forefront of AI development and make a real impact.
- Qualifications: Strong background in machine learning and experience with Large Language Models.
- Other info: Collaborative culture with frequent team gatherings and a focus on wellbeing.
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 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 working on our data team focused on the quality of the datasets being delivered for training our models. This is a hands-on role where your #1 mission would be to improve the quality of the pretraining datasets by leveraging your previous experience, intuition and training experiments. This includes synthetic data generation and data mix optimization. You will closely collaborate with other teams like Pretraining, Postraining, Evals, and Product to define high-quality data needs that map to missing model capabilities and downstream use cases. Staying in sync with the latest research in the fields of dataset design and pretraining is key to success in this role. You will constantly lead original research initiatives through short, time-bounded experiments while deploying highly technical engineering solutions into production. With the volumes of data to process being massive, you will have a performant distributed data pipeline together with a large GPU cluster at your disposal.
YOUR MISSION
To deliver large, high-quality, and diverse datasets of natural language and source code for training poolside models and coding agents.
RESPONSIBILITIES
- Follow the latest research related to LLMs and data quality in particular. Be familiar with the most relevant open-source datasets and models.
- Design and implement complex pipelines that can generate large amounts of data while maintaining high diversity and optimizing the resources available.
- Closely work with other teams such as Pretraining, Posttraining, Evals and Product to ensure short feedback loops on the quality of the models delivered.
- Suggest, conduct and analyze data ablations or training experiments that aim to improve the quality of the datasets generated via quantitative insights.
SKILLS & EXPERIENCE
- Strong machine learning and engineering background
- Experience with Large Language Models (LLM), including:
- Understanding of transformer architectures and how LLMs learn
- Data ablations and scaling laws
- Mid-training and Post-training techniques
- Training reasoning and agentic models
- Experience with evals tracking model capabilities (general knowledge, reasoning, math, coding, long-context, etc)
- Experience in building trillion-scale pretraining datasets, and familiarity with concepts like data curation, deduplication, data mixing, tokenization, curriculum, impact of data repetition, etc.
PROCESS
- Intro call with one of our Founding Engineers
- Technical Interview(s) with one of our Members of Engineering
- 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 employer: poolside
Contact Detail:
poolside Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Member of Engineering
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working at Poolside. A friendly chat can open doors and give you insider info on what they're really looking for.
✨Tip Number 2
Show off your skills! If you've got a portfolio or any projects related to data quality or machine learning, make sure to highlight them during interviews. We love seeing practical examples of your work!
✨Tip Number 3
Stay updated with the latest research! Dive into papers about LLMs and dataset design. Being able to discuss recent findings shows your passion and commitment to the field, which we totally appreciate.
✨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 joining our team at Poolside.
We think you need these skills to ace Member of Engineering
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let us see your enthusiasm for artificial intelligence and data quality. Share any relevant projects or experiences that highlight your commitment to pushing the boundaries in this field.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter to reflect the specific skills and experiences mentioned in the job description. We want to see how your background aligns with our mission at Poolside!
Highlight Collaboration Skills: Since you'll be working closely with various teams, emphasise your ability to collaborate effectively. Mention any past experiences where teamwork led to successful outcomes, especially in engineering or research contexts.
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!
How to prepare for a job interview at poolside
✨Know Your Stuff
Make sure you brush up on the latest research related to Large Language Models (LLMs) and data quality. Familiarise yourself with relevant open-source datasets and models, as this will show your passion and commitment to the field.
✨Showcase Your Experience
Be ready to discuss your hands-on experience with machine learning and engineering. Highlight any projects where you've designed complex data pipelines or worked with large-scale GPU clusters. Concrete examples will help you stand out!
✨Collaborative Spirit
Since the role involves working closely with various teams, demonstrate your ability to collaborate effectively. Share examples of how you've successfully worked in cross-functional teams and maintained short feedback loops to improve project outcomes.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to dataset design and pretraining. This not only shows your interest but also gives you a chance to gauge if their values align with yours. Plus, it makes for a great conversation starter!
