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