Deep Learning Research Engineer

About the role

At Plumerai, we make it easy and affordable for developers to add highly accurate AI to their camera devices, enabling them to create amazing new products. Major brands deploy our advanced computer vision models on millions of smart home cameras in the field and we're rapidly expanding into other sectors, such as commercial security, elderly care and retail. We combine our on-device Tiny AI software with our cloud-based Vision Language Models, to provide our customers with powerful AI features, including People Detection, Video Search, Familiar Face Identification, AI Captions and more. We prioritize on-device inference, to enable low-power, super-efficient and private AI products. Plumerai leads on accuracy, even when compared to large players, such as Google Nest.

We're a small, high-performing team based in London and Amsterdam, with plans to double in size over the next year. Following our recent $8.7M Series A, backed by world-class investors including Tony Fadell, Hermann Hauser, and Zoubin Ghahramani, we have multiple years of runway and rapidly growing recurring revenue.

We’re in a period of significant growth and are expanding our engineering team across multiple levels and specialisms. This is one of several hires we’re making as we scale the company.

🎥 GET TO KNOW PLUMERAI

Meet the team and see what it's like to work here:

▶️ Life at Plumerai https://www.youtube.com/watch?v=AvWRfkXQ3NQ

▶️ What we're building https://www.youtube.com/watch?v=XFJ8W5K5Mz4

Learn more here: Plumerai https://plumerai.com

👉 Read more: TechCrunch https://techcrunch.com/2024/10/06/tony-fadell-backed-plumerai-brings-on-device-ai-to-home-security-cameras, Series A funding announcement https://blog.plumerai.com/2025/09/plumerai-raises-usd8-7m-series-a/

Role description

We are looking for a Deep Learning Research Engineer that can help us develop state of the art AI products. This can involve anything from improving our training algorithms, training and integrating multimodal LLMs, building our data pipeline, designing new model architectures to using tried and tested ML approaches and coming up with clever algorithms. You will help us build new AI features that will be shipped to millions of camera devices in the field. Together we are building the most advanced AI for embedded devices.

What you will be doing

- We combine our Tiny AI with multimodal LLMs to enable our advanced AI features for our customers. You will use and improve multimodal LLMs to achieve new functionality for our customers and optimize their deployments (cloud and edge).

- Some of our deep learning models are truly tiny - the memory footprint of our smallest computer vision model is just 1MB. You will train and design more accurate models, while also enabling new and more complex AI applications on low-cost and low-power hardware.

- You will improve our data pipeline, model architectures and training software. Sometimes there is relevant literature available, but novel approaches and clever hacks are often required for the problems that we are working on.