Member of Engineering (Multimodality - Research Lead)

Remote (EMEA/East Coast)Senior

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 open-weight frontier models that power them.

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 build a new team from the ground up, owning multimodality at Poolside: teaching our frontier coding model to see. Multimodality is becoming increasingly important in frontier models, and this is a chance to shape this capability in Poolside’s models from an early stage. To deliver it, you'll draw on our powerful model factory, thousands of GPUs, and our strong research team.

The near-term focus is image input — the capability most useful to SWE and general agents — reading a design and speccing it out, writing and verifying the code that makes that design real, understanding the plots and diagrams in a paper. You'll set the technical direction, starting with pragmatic adapter-based approaches and evolving toward native multimodality over time. You'll partner closely with across teams inside Applied Research, and work in a newly-formed team alongside one of our talented founding engineers to bootstrap our multimodality efforts.

YOUR MISSION

To bring multimodality to Poolside's models, starting with image input and building toward native multimodal understanding.

RESPONSIBILITIES

Own Poolside's multimodality direction and capability adoption
Ship our first image-input capabilities
Chart and drive the path from adapter-based to native multimodality
Collaborate on custom evaluations and datasets for multimodal SWE capabilities
Run experiments end to end: hypothesis, implementation, training at scale, analysis
Partner with evals, architecture, data, and post-training teams to land multimodality in Poolside models

SKILLS & EXPERIENCE

Experience in end-to-end training of production-grade VLMs
Strong LLM training fundamentals: transformers and distributed training at scale
Strong Python programming skills
Pragmatic and high-ownership: you reach for the simplest thing that works and thrive in greenfield ambiguity

Nice to have:

Leadership and 0-1 experience, especially with substantial breadth, ownership, and hands-on contributions
Distinguished research background on VLMs or multimodal models
Understanding of the challenges of native multimodality
Experience building agentic computer use systems