---
title: 'Member of Engineering (Pre-training / Data Research) at Poolside'
canonical: 'https://feeny.ai/job/member-of-engineering-pre-training-data-research-poolside-emea-east-coast-5jmpzg09bqd9'
type: 'job'
last_seen: '2026-09-09'
---

# Member of Engineering (Pre-training / Data Research) at Poolside

- **Company:** Poolside
- **Location:** EMEA / East Coast, United States
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-05-19
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/poolside/819757ab-8e03-4c7b-8fed-be7451372cb4/application

**Skills:** Python, Machine Learning, Large Language Models (LLM), Transformer Architectures, Data Ablations, Distributed Data Pipelines, GPU Clusters, Prompt Engineering, Data Curation, Deduplication, Data Mixing, Tokenization, Scientific Paper Authoring

> This role focuses on improving the quality of pretraining datasets for AI models through synthetic data generation and data mix optimization. The engineer will design complex pipelines, conduct experiments to analyze data quality, and collaborate with cross-functional teams to ensure high-quality data delivery for t...

## Job description

## 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’ll 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’ll 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'll have a performant distributed data pipeline together with a large GPU cluster at your disposal.

Curious about the tech? Take a deep dive into our pretraining data work in our Laguna M.1/XS.2 Technical Report. https://arxiv.org/abs/2605.27605

## 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.
- Excellent programming skills in Python
- Strong prompt engineering skills
- Experience working with large-scale GPU clusters and distributed data pipelines
- Strong obsession with data quality
- Research experience:
- Author of scientific papers on any of the topics: applied deep learning, LLMs, source code generation, etc. - is a nice to have
- Can freely discuss the latest papers and descend to fine details
- Is reasonably opinionated

## 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 & dependents
- Company-provided equipment
- Well-being, always-be-learning & home office allowances
- Frequent team get togethers
- Diverse & inclusive people-first culture

## About Poolside

## Company Overview
- **One-liner**: Poolside builds frontier AI foundation models and agentic systems for enterprise software engineering, deployed entirely within customer infrastructure.
- **Entity Type**: Private (Series B, $626M total funding)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Jason Warner (CEO & Co-founder), Eiso Kant (Co-CEO & Co-founder, CTO)

## Core Business
- **Primary industry/industries**: Artificial intelligence, enterprise software development
- **Target customers**: Large enterprises, government agencies, and organizations with complex, high-consequence software environments (multi-cloud, legacy systems, air-gapped networks)
- **Mission or purpose statement**: “For artificial general intelligence to drive abundance for humanity” – starting with software engineering as the strategic beachhead.

## Products & Services
- **Poolside Assistant**: Agent-based code assistance integrated into VS Code, Visual Studio, or any IDE supporting the Agent Client Protocol (ACP). Capable of planning, refactoring, testing, and incident analysis within the developer’s environment.
- **Poolside Chat**: Browser-based chat interface for code-related questions and agent interactions outside the IDE.
- **Poolside Agent CLI (`pool`)**: Command-line tool to run and manage agents in headless environments (CI/CD pipelines, automation workflows).
- **Poolside Console**: Browser-based administrative interface for governance, metrics, and system management.
- **APIs**: Programmatic access to integrate Poolside models, agents, and knowledge connectors into custom tools and automation.
- **Foundation Models**: Custom models trained using reinforcement learning from code execution feedback, optimized for long-horizon software tasks.
- **Data & Knowledge Connectors**: Integration with repositories, databases, data warehouses, and private corpora within strict security boundaries.
- **Forward Deployed Research Engineers (FDRE)**: Embedded experts who help customers design, build, and operate AI systems on-site.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding $626 million as of October 2024, including a $500M Series B led by 19 investors [poolside.ai](https://www.poolside.ai/)
- **Notable Investors/Partners**: Series A led by Felicis, Xavier Niel, and others; Series B participants include 19 investors. Seed round led by Redpoint. [linkedin.com](https://www.linkedin.com/company/poolsideai)
- **Growth Signals**: Headcount grew 89.1% year-over-year to 165 employees; 19 active job postings (up 171% year-over-year). Operates across 20 countries. [linkedin.com](https://www.linkedin.com/company/poolsideai)

## Competitive Advantages
- **Security-first deployment**: Models run entirely within the customer’s VPC or on-premises, ensuring code and data never leave the customer’s control. Role-based access control for humans and agents by default. [poolside.ai](https://www.poolside.ai/)
- **Enterprise governance**: Auditability, policy enforcement, and traceability aligned to enterprise review boards and CISO requirements.
- **Outcome ownership model**: Poolside takes joint responsibility for adoption and business impact, rather than simply handing off models.
- **Specialization in high-consequence software**: Battle-tested in the toughest environments (defense, regulated industries), with a focus on long-horizon reasoning and multi-step planning.

## Strategic Focus
- **AGI through software**: The company believes the fastest path to human-level intelligence runs through mastering software engineering, then expanding to other domains.
- **Agentic organizations**: Helping enterprises become “agentic organizations” by embedding AI agents across the development lifecycle.
- **Continued model scaling**: Investing in foundation model research (pre-training, post-training, reinforcement learning) and infrastructure.
- **Global talent acquisition**: Growing a distributed workforce with hubs in the US, UK, France, Netherlands, and other European countries.

## Why Work Here
- **High employee satisfaction**: 5.0/5.0 rating on LinkedIn (based on 1 review) across work-life balance, compensation, culture, and career growth. [linkedin.com](https://www.linkedin.com/company/poolsideai)
- **Cutting-edge research**: Work alongside world-class talent at a frontier AI lab, solving the hardest problems in software engineering.
- **Hybrid/remote flexibility**: The company operates across 20 countries, suggesting a distributed-first approach with regional hubs.
- **Growth trajectory**: 89% headcount growth in the past year, with active hiring across engineering (agent harness, pre-training, reinforcement learning, design engineering) and support engineering roles.
- **Impactful mission**: Directly contribute to the development of AGI for abundance, starting with transformative enterprise software.

## Sources
1. [poolside.ai](https://www.poolside.ai/)
2. [linkedin.com](https://www.linkedin.com/company/poolsideai)
3. [docs.poolside.ai](https://docs.poolside.ai/get-started/overview)
4. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/poolside)

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