poolside

Poolside is building a world where AI powers economically valuable work and scientific progress, aiming to accelerate AGI development by enhancing the developer experience with agentic systems, coding assistants, and frontier models. This role is part of the Model Factory team, responsible for building and maintaining the data and analytics systems that underpin model development, such as agent trajectory stores and experiment configuration registries. The work involves close collaboration with researchers to understand their needs and improve data and analytics, directly contributing to faster model development cycles. This position offers the opportunity to own foundational systems, build at scale, and work on greenfield projects as well as extending existing systems to meet demanding performance and reliability requirements.

Poolside is building a world where AI drives economically valuable work and scientific progress, aiming to accelerate the development of Artificial General Intelligence (AGI). We focus on reshaping the developer experience with agentic systems, coding assistants, and frontier models. This role is an opportunity to build and lead a new team focused on multimodality, specifically teaching our frontier coding model to understand and process image inputs. You will shape this critical capability from its early stages, leveraging our extensive resources including a powerful model factory, thousands of GPUs, and a strong research team. The initial focus will be on image input capabilities, such as interpreting designs, generating and verifying code, and understanding diagrams, with a long-term vision of evolving towards native multimodal understanding.

Poolside is building a world where AI drives economically valuable work and scientific progress, aiming to accelerate software development with agentic systems, coding assistants, and frontier models. This role is on the compute team, focusing on optimizing GPU workload scheduling and inference serving. You will partner with the inference team to enhance throughput and latency for evaluations and reinforcement learning, collaborate with the scalability team to stabilize large-scale fault-tolerant training, and work closely with the infrastructure team to ensure GPU nodes are healthy and fully utilized. Your work will directly impact research velocity and contribute to the company's mission of building frontier models.

Poolside is building a world where AI is the engine behind economically valuable work and scientific progress, aiming to accelerate software development with agentic systems, coding assistants, and frontier models. This role focuses on designing and implementing the build, CI/CD, and tooling infrastructure that researchers and engineers rely on daily. You will join the infrastructure team as the second engineer focused on developer experience, partnering closely with the current owner while sharing on-call, incident response, and platform ownership. The mission is to make it seamless for researchers and engineers to build, test, and ship by optimizing the monorepo, ensuring reproducible builds, and maintaining a coherent Python ecosystem through automation.

Poolside is building a company to create Artificial General Intelligence, aiming to accelerate software development through agentic systems and frontier models. We are a distributed team across Europe and North America, with monthly in-person collaboration in Paris and annual off-sites. Our multidisciplinary team is united by a deep care for our work, fostering a culture of hard work, intellectual curiosity, and kindness. We are seeking a Member of Engineering to join our Platform engineering team, focusing on our sandboxed agent execution environment. This role is crucial for accelerating experimentation and foundation model training by designing, developing, and operating services and systems that enable running agent sessions safely and at scale.

Poolside is building a company to create Artificial General Intelligence, aiming to accelerate software development through agentic systems, coding assistants, and frontier models. This role focuses on improving the quality of pretraining datasets for our models. You will leverage your experience and conduct training experiments to enhance dataset quality, including synthetic data generation and data mix optimization. Collaboration with Pretraining, Posttraining, Evals, and Product teams is essential to define data needs that address missing model capabilities and downstream use cases. Staying current with research in dataset design and pretraining is crucial, as you will lead original research initiatives and deploy technical engineering solutions into production, utilizing a performant distributed data pipeline and a large GPU cluster.

Poolside is building a company to create Artificial General Intelligence, aiming to accelerate software development through agentic systems, coding assistants, and frontier models. This role is part of the Applied Research team, focused on transforming pre-trained Large Language Models (LLMs) into well-aligned and highly capable AI systems specifically for coding and software development. You will be involved in building data pipelines and environments for agentic use cases, researching and implementing post-training algorithms, and designing experiments to test hypotheses, with access to significant GPU resources.

Poolside is building a company to create Artificial General Intelligence, aiming to be a leader in applied research, engineering, infrastructure, and large-scale deployment. We are a remote-first team across Europe and North America, coming together monthly for in-person collaboration. Our R&D and production teams value strong software development knowledge and believe good engineering drives faster iteration. This role focuses on evaluation, a critical pillar for building frontier models and products. You will design and implement a powerful, self-serve evaluation framework to enable easy building, running, and insight extraction from evaluations at scale, supporting our research and development efforts.

Poolside is building a company to create Artificial General Intelligence, aiming to be a leader in applied research, engineering, infrastructure, and large-scale deployment. We are a remote-first team spread across Europe and North America, with monthly in-person meetups and biannual offsites. Our teams blend research and engineering expertise, emphasizing strong software development knowledge and a commitment to system quality for faster iteration and compounded efforts. This role is crucial in ensuring our progress translates into meaningful improvements for our users.

Poolside is building a world where AI drives economically valuable work and scientific progress, aiming to accelerate software development through agentic systems and frontier models. This role is a core part of the Pretraining Data team, responsible for building and scaling the Model Factory, a system for rapid training, scaling, and experimentation with foundation models. The primary mission is to architect and maintain high-performance pipelines that transform trillions of raw tokens into high-quality dataset "fuel" for models. This involves engineering ingestion, deduplication, and streaming systems for petabyte-scale data, bridging the gap between raw web crawls and GPU clusters, and directly influencing model performance through superior data modeling and distributed pipeline optimization. Collaboration with Pretraining, Posttraining, Evals, and Product teams is key to generating high-quality datasets that address missing model capabilities and downstream use cases.

Poolside is building a company to achieve Artificial General Intelligence, focusing on accelerating software development through agentic systems and frontier models. This role is on the data team, with the primary mission to improve the quality of datasets used for training models across the entire training cycle. The focus is on generating synthetic data at scale and determining the best strategies to leverage this data for training large models. You will collaborate with various teams to define data needs that address missing model capabilities and downstream use cases, staying current with research in synthetic data generation and LLM training. This is a hands-on role involving leading research initiatives through experiments and deploying engineering solutions into production, with access to performant distributed data pipelines and large GPU clusters.