About this Deep Learning Research Engineer role at Plumerai
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:
Learn more here: Plumerai
👉 Read more: TechCrunch, Series A funding announcement
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.
You will use our Kubernetes cluster to deploy PyTorch and TensorFlow training jobs, Snowflake and Dataflow to build datasets, tools like Streamlit to prototype new demos (try one of our live demos here) and lots of GPUs on GCP for training new models and auto-labeling data.
What You Need
Strong engineering experience with proficiency in Python.
Comfortable with frameworks such as PyTorch, TensorFlow, Keras, or JAX.
Strong experience with computer vision and multimodal LLMs.
Trained neural networks that moved into production.
Nice To Have
Industry experience with efficient inference deployments (cloud or edge).
Experience with Deep Reinforcement Learning.
We only consider applicants who are currently based in, or willing to relocate to, London or Amsterdam. We have flexible working hours and work together from our offices on at least 2 fixed days per week.
What we offer
Competitive salary.
Generous equity stake in the company.
Relocation assistance and visa sponsorship is provided.
Choose your own laptop and equipment.
25 days of paid vacation time in addition to bank holidays.
Ability to attend top research conferences like NeurIPS, ICML and CVPR.
Process
Talent Screen
Technical Round I
Technical Round II
Cross Team & Executive Interview
