Poolside
Member of Engineering (Pre-training / Data Acquisition)

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Member of Engineering (Pre-training / Data Acquisition)
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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About The Role
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.
YOUR MISSION
To deliver the highest-quality, diverse, and most comprehensive data corpus to fuel the pre-training of frontier models for software development.
Responsibilities
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


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Skills & Experience
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, robots.txt adherence, 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 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 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
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