Member of Engineering, Human Data

About poolside

In this decade, the world will create artificial intelligence that reaches human level intelligence (and beyond) by combining learning and search. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will determine who survives and wins. 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 and engineering at scale. They will create powerful economic engines. 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.

poolside exists to be one of these companies - to build a world where AI will drive the majority of economically valuable work and scientific progress.

We believe that software development will be the first major capability in neural networks that reaches human-level intelligence because it's the domain where we can combine Search and Learning approaches the best.

At poolside we believe our applied research needs to culminate in products that are put in the hands of people. Today we focus on building for a developer-led increasingly AI-assisted world. We believe that current capabilities of AI lead to incredible tooling that can assist developers in their day to day work. We also believe that as we increase the capabilities of our models, we increasingly empower anyone in the world to be able to build software. We envision a future where not 100 million people can build software but 2 billion people can.

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About our team

We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year.

Our R&D and production teams are 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

As a Member of Engineering (Human Data), you will lead the development and management of high-quality data labeling pipelines that support our large language models. This role involves building an internal labeling team, working closely with vendors, and designing scalable processes for data annotation.

While the position does not include customer-facing responsibilities, your work will be critical to the success of our AI models, ensuring that they are trained on top-tier labeled data using crowdsourcing and other data collection techniques.

Your mission

To build and optimize scalable data labeling pipelines that power the success of our machine learning models.

Responsibilities

• Design, develop, and implement scalable data labeling pipelines that integrate into model training workflows. Manage and expand the internal data labeling team to meet the company's growing needs
• Collaborate with external vendors to source and manage crowdsourced data labeling efforts, ensuring timely and high-quality delivery
• Monitor and improve labeling processes by conducting experiments, ensuring data quality, and optimizing performance across labeling projects
• Set up metrics and QA processes to evaluate the quality of labeled data and continuously improve output
• Work cross-functionally with researchers and engineers to align labeling pipelines with model training needs
• Identify new tools and technologies to streamline labeling processes and increase efficiency

Skills & Experience

• Experience with designing and managing data labeling processes, with a strong emphasis on crowdsourcing solutions
• 2+ years of experience in a technical role such as Data Engineer, Data Scientist, Technical Project Manager, or similar, ideally in machine learning/data-focused environments
• Familiarity with managing vendors and crowdsourcing platforms to handle large-scale data labeling efforts
• Strong understanding of data quality metrics such as accuracy, precision, recall, and F1 score
• Proven ability to develop complex pipelines with multiple stages, particularly for data annotation and machine learning training
• Experience with cloud platforms and tools such as AWS, GCP, Kubernetes, and CI/CD systems is a plus
• Ability to collaborate with technical teams and ensure labeling processes align with overall model development needs
• Mandatory experience with crowdsourcing platforms (e.g., ScaleAI, Toloka, or similar) for data labeling
• Strong problem-solving skills and ability to work independently in a fast-paced environment

Process

• Intro call with Eiso, our CTO & Co-Founder
• Technical Interview(s) with one of our Founding Engineers
• Team-fit call with Beatriz, our Head of People
• Final interview with Eiso, our CTO & Co-Founder

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