[Hiring] Senior CV ML Engineer @Raw Ventures

Role Description

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.

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

Qualifications

• 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

Benefits

• Fully remote, CET hours
• Real product impact at scale
• Direct contact with leadership and engineering team
• AI-augmented development culture
• Competitive compensation, discussed individually