As a Senior AI Research Engineer, you'll drive innovation in embedded artificial intelligence by developing compact, high-performance models and refining multimodal large language systems. Your work will directly impact AI features deployed across millions of edge devices, balancing accuracy with extreme efficiency constraints.
What You'll Do
- Enhance and deploy multimodal LLMs to enable new capabilities, optimizing performance across cloud and edge environments
- Design and train deep learning models with minimal memory footprint—down to 1MB—for deployment on low-power hardware
- Refine data pipelines, model architectures, and training infrastructure to improve model accuracy and efficiency
- Run distributed training jobs using PyTorch and TensorFlow on Kubernetes clusters
- Construct and manage datasets using Snowflake and Dataflow
- Develop interactive prototypes with Streamlit to demonstrate new AI features
- Utilize GCP-hosted GPUs for model training and automated data labeling
- Support the delivery of AI-powered features to deployed camera systems at scale
Requirements
- Minimum of five years of professional software engineering experience with strong Python proficiency
- Proven expertise with deep learning frameworks such as PyTorch, TensorFlow, Keras, or JAX
- Hands-on experience in computer vision and multimodal LLMs
- Track record of training neural networks that have transitioned into production environments
Preferred Qualifications
- Industry experience deploying models efficiently in cloud or edge settings
- Background in Deep Reinforcement Learning
Technical Environment
Python, PyTorch, TensorFlow, Keras, JAX, Kubernetes, Snowflake, Dataflow, Streamlit, Google Cloud Platform, GPU acceleration
Benefits
- Competitive salary
- Generous equity stake in the company
- Relocation support available
- Choice of laptop and equipment
- 25 days paid vacation plus bank holidays
- Opportunity to attend leading research conferences including NeurIPS, ICML, and CVPR
Work Mode
This is a hybrid role with office locations in London and Amsterdam. The team meets in person on at least two fixed days per week, with flexible scheduling for the remainder. Work hours are adaptable to support productivity and work-life balance.
Company Culture
We practice full-stack AI development, combining deep theoretical insight with rapid, practical implementation. Our focus is on fast iteration, high accuracy, energy efficiency, and strong privacy standards. Innovation is central, and engineers are empowered to ship impactful features frequently.
