Senior CV ML Engineer

We’re hiring for a Raw Ventures portfolio company — a computer vision platform that processes millions of images from real-world deployments and turns them into actionable insights for industry partners. Series A. The role You own the CV detection stack end-to-end — models, data, labeling and validation processes, evaluation methodology, production serving. You close the loop with the validators team and stay close to customers and product to keep model work tied to real-world value. Senior autonomy: you set direction and ship. Stack PyTorch • YOLO 11 • Roboflow • Triton • CUDA • AWS • EKS • S3 • Docker • Claude Code What you’ll do Own the full model lifecycle — data, labeling, training, evaluation, deployment, monitoring, feedback Improve detection/segmentation models across diverse real-world conditions, lighting, environments Build labeling, testing, and validation processes with the validators team — taxonomies, guidelines, QA loops, active learning Define evaluation methodologies; identify weak spots and fix them Stay close to customers and product — what they pay for shapes model priorities Productionize models on Triton — latency, throughput, cost Drive research direction: pretraining, architectures, multi-stage pipelines What we expect 5+ years CV/ML with senior depth Deep understanding of the full CV model lifecycle Track record of high-quality production CV models — robust across real conditions, edge cases handled Methodological eye — you spot when evaluation, labeling, or training is broken and fix it Product and business sense — you understand how models make money, don’t chase accuracy that doesn’t move the business Hands-on YOLO (YOLO 11 ideal, any recent version counts) Experience designing labeling/annotation processes with annotation teams Roboflow workflows or comparable (dataset versioning, labeling, augmentation) Triton deployment and optimization (or comparable serving infra) MLOps fundamentals — experiment tracking, model versioning, evaluation, production monitoring Strong PyTorch, production-grade Python (not just notebooks) Russian — fluent or native (required) English B1+ Central European working hours Nice to have Active learning, semi-supervised methods • Self-supervised pretraining for domain adaptation • Model quantization / pruning / ONNX / TensorRT • Scientific imaging or biology • Multi-camera / multi-view systems • Edge inference on devices What we offer Fully remote, CET hours Real product impact at scale Direct contact with leadership and engineering team AI-augmented development culture Competitive compensation, discussed individually