Member of Engineering (Multimodality - Research Lead)

About Poolside

Poolside exists 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 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 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

Benefits

• Fully remote work & flexible hours
• 37 days/year of vacation & holidays
• Health insurance allowance for you & dependents
• 16 weeks of flexible, full-pay parental leave
• Well-being, always-be-learning & home office allowances
• Company-provided equipment
• Frequent team get-togethers
• Diverse & inclusive people-first culture