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# Founding AI Engineer

- **Company:** SR2
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Language Modeling, Software Product Management, Deep Learning, Data Pipelines
- **Published:** September 10, 2026
- **Apply:** https://www.collegerecruiter.com/job/2847390112-founding-ai-engineer

## About the Role

* Substantial hands-on experience training or fine-tuning multimodal or Vision-Language Models.
 * Strong understanding of transformer architectures and what happens beneath high-level model APIs.
 * Experience with models or approaches such as LLaVA, CLIP, Flamingo or comparable multimodal architectures.
 * Experience working with video data and modelling temporal context, potentially involving temporal attention, optical flow or video representation learning.
 * A record of deploying deep-learning models into production environments where latency, reliability and cost matter.
 * The ability to translate promising research into robust, maintainable systems.
 * Comfort operating in a high-ownership, zero-to-one environment with incomplete information and rapidly changing priorities.
 * A preference for shipping useful technology over producing research that remains theoretical.

An interest in games, simulation or complex interactive environments is important.

 * Architect the core intelligence behind a new category of autonomous agent.
 * Work on a difficult multimodal problem involving vision, video, reasoning and action.
 * Own technical decisions across the full model lifecycle.

## Description

* Design model inputs that capture temporal context across video sequences rather than treating gameplay as a collection of static frames.
 * Build and automate data pipelines that convert raw gameplay into high-quality, annotated training datasets.
 * Optimise models for the latency, throughput and inference-cost requirements of real-time gameplay.
 * Create evaluation suites and benchmarks that measure genuine agent capability.
 * Take research ideas through experimentation, validation and production deployment.
 * Help define the technical architecture, modelling strategy and engineering standards of an early-stage AI product.


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