AI code generation models · San Francisco / Paris
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.
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.
You'll be working alongside our pre-training data team, focused on one of the most foundational challenges in training frontier LLMs: acquiring the best possible pre-training data.
The data we collect is upstream of everything. It directly shapes the capability of the models we train. As our first dedicated data acquisition engineer, you will spearhead and evolve systems that crawl the web at massive scale, rapidly ingest data from strategic partnerships, and build specialized tooling to maximize recall from high-value sources. You'll collaborate closely with pre-training data researchers and engineers to ensure that our sourcing of data maps to our training needs, to ensure we have the most capable pre-trained models.
To deliver the highest-quality, diverse, and most comprehensive data corpus to fuel the pre-training of frontier models for software development.
Design, build, and operate a large-scale web crawler responsible for acquiring all openly accessible data on the internet
Develop specialized deep crawlers targeting high-value sources to improve recall and coverage
In collaboration with data researchers, own a long-term road map for data acquisition
Build observability, monitoring, and debugging tooling to ensure reliability and transparency across crawl infrastructure
Collaborate with pre-training, post-training, and evaluations teams to align data acquisition priorities with model training needs
Build high-throughput ingestion pipelines for rapidly onboarding partner data and evaluating it for quality
Strong distributed systems background with proven experience building and operating large-scale infrastructure — data pipelines, web crawlers, or similar
Proficiency in Python, and comfortable optimizing performance and debugging complex systems under production conditions
Hands-on experience with web crawling or large-scale data extraction: understanding of HTTP protocols, distributed job queues, and data parsing at scale
Familiarity with cloud platforms (AWS) and container orchestration (Kubernetes, Docker) for deploying and managing high-throughput workloads
Awareness of the non-technical dimensions of internet-scale crawling: data privacy and responsible crawl practices
Nice to have:
Prior experience pre-training LLMs
Experience in building trillion-scale SOTA pre-training datasets
Experience translating research to production at scale
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
Fully remote work & flexible hours
37 days/year of vacation & holidays
16 weeks of flexible, full-pay parental leave
Health insurance allowance for you & dependents
Company-provided equipment
Well-being, always-be-learning & home office allowances
Frequent team get togethers
Diverse & inclusive people-first culture