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Poolside

Member of Engineering (Multimodality - Research Lead)

United Kingdom
Posted about 15 hours ago
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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.

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I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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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

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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

PROCESS

  1. Intro call with one of our Co-Heads of Applied Research
  2. Technical Interviews with Founding Engineers and Members of Engineering
  3. Team fit call with the People team
  4. Final interview with our Co-CEO and Co-Founder, Eiso

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
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Skills

Vision Language Models
LLM Training
Transformers
Distributed Training
Python
Multimodality
Machine Learning
Research
Software Development
Agentic Systems
Model Training at Scale
Data Analysis
Experimental Design

Location

United Kingdom

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