30 - Computer Vision Engineer
About the role
We're looking for a computer vision engineer to join a small, autonomous team at an European startup building AI-powered machinery for automated visual inspection and sorting of physical materials. Their systems are already live in production at multiple client sites — this is not a research project, it's real machines making real-time decisions.
The software stack is in good shape overall. The gap is specifically on computer vision. You'll work alongside the current computer vision engineer on real-time detection of features and attributes on physical items moving through the system. Real-time performance is a hard requirement — this is not an offline or batch process.
The team is fast-paced and results-driven. You'll be expected to own your topic without close supervision.
What you'll work on
Real-time object detection and segmentation models running on live production machinery
Detection of fine-grained features and attributes on varied, irregular physical items
Model optimization for real-time inference performance
The full model lifecycle: training, registry, deployment, and monitoring
Must-have skills
CV models
RF-DETR
YOLO segmentation
Sliced/SAHI-style batched YOLO inference
CLIP-style embeddings
Cloud & MLOps
Google Cloud Platform (Vertex AI)
MLflow for model registry
TensorRT
Technical foundation
Image processing
Linux proficiency
Docker containerization
Who you are
Autonomous — comfortable owning a topic without close supervision
Result-oriented — you measure yourself by what ships and works
Builder mindset — you'd rather get something running than write a perfect spec
ATTENTION UPON DROPPING YOUR APPLICATION:
Please, immediately send an email to [email protected] with subject 'Computer Vision Engineer – Your Name' sharing concrete examples of real-time computer vision work you've done in production.
We're specifically interested in:
Real-time detection or segmentation systems you've shipped — the model architecture (RF-DETR, YOLO, or equivalent), the latency constraints, and how you met them
Sliced/SAHI-style inference you've implemented — the use case, why tiling was needed, and how you handled the throughput trade-offs
CLIP-style embedding work — what you used the embeddings for (classification, retrieval, attribute detection) and how it performed in production
TensorRT optimization you've done — what you converted, the speedup you achieved, and any precision or compatibility issues you solved
Model lifecycle setups you've built or maintained on GCP (Vertex AI) and MLflow — how models moved from training to production
Deployments on Linux/Docker in constrained or edge environments — especially anything running on or near physical hardware