Middle Computer Vision / ML Engineer

Middle Computer Vision & ML Engineer — Founding Team

About Us

We are a newly funded MilTech startup building an on-premises platform for automated annotation of petabytes of UAV video and training computer vision models for autonomous flight. We have a strategic partnership with the largest Ukrainian UAV manufacturer and access to what is the largest privately owned UAV video dataset in the world. We are at the very beginning — the technical plan exists, the funding is secured, the data is real — and now we need the right people to turn it into reality.

The Role

We are looking for a Middle CV/ML Engineer to join as one of the first engineers on the team. You will work closely with the technical lead to build the ML pipeline from the ground up — implementing core components across data processing, model training, and deployment.

This is an early-stage startup, so the tech stack and processes are still being shaped. You will have real input into the tools and approaches we adopt, and your work will directly define how the platform operates. We are looking for someone who is hands-on, curious, and comfortable building things that don't exist yet.

What You Will Work On

Data Pipeline & Preprocessing
- Build video preprocessing pipelines: clip segmentation, frame extraction, scene detection, quality filtering
- Implement embedding extraction and data curation workflows for selecting diverse training subsets from a massive video corpus
- Prepare and export datasets in standard formats for training and annotation

Model Training & Experimentation
- Train and evaluate computer vision models for object detection, classification, tracking, and segmentation
- Run experiments, track results, and iterate on model architectures and hyperparameters
- Implement data augmentation strategies for robustness across weather, lighting, and occlusion conditions
- Contribute to distributed multi-GPU training pipelines

Active Learning & Annotation Support
- Implement auto-labeling pipelines with confidence-based routing
- Build tooling around the annotation platform (pre-annotation, format conversion, quality validation)
- Maintain automated quality checks for label consistency

Edge Deployment
- Export and optimize models for inference on NVIDIA Jetson (ONNX, TensorRT, FP16/INT8 quantization)
- Run accuracy and latency benchmarks on edge devices
- Help maintain the model delivery pipeline from training to deployment

What We Are Looking For

Must Have
- 3+ years of hands-on experience in computer vision and deep learning
- Solid PyTorch skills — comfortable writing training loops, custom datasets, and model code
- Experience with at least one detection or segmentation framework (YOLO family, Detectron2, MMDetection, or similar)
- Understanding of model evaluation metrics (mAP, IoU, precision/recall)
- Familiarity with experiment tracking (MLflow, W&B, or similar)
- Python as primary language; clean, well-structured code
- Comfortable working in Linux and containerized environments (Docker)

Nice to Have
- Model optimization experience (ONNX export, TensorRT, quantization)
- NVIDIA Jetson or other edge/mobile deployment
- Multi-object tracking (ByteTrack, DeepSORT, or similar)
- Experience with video processing pipelines (FFmpeg, OpenCV, decord)
- Data curation or annotation tools (FiftyOne, CVAT, Label Studio)
- Distributed training (DDP, Ray Train)
- UAV / drone / robotics or MilTech domain experience

Proposed Tech Stack

This is our starting point — the stack is still being finalized, and you will have input into the choices we make.

- Training: PyTorch, distributed training (DDP/FSDP) 
- Data processing: Ray Data or equivalent scalable framework 
- Data curation: FiftyOne, embedding-based selection 
- Annotation: CVAT with pre-annotation and active learning 
- Experiment tracking: MLflow 
- Edge: ONNX → TensorRT → NVIDIA Jetson 
- Infrastructure: Kubernetes, on-prem GPU cluster 
- Storage: S3-compatible object storage (Ceph) 

What We Offer

- Join an early-stage team where your contributions shape the product from day one
- Work across the full ML pipeline — not just one narrow piece
- The largest privately owned UAV video dataset in the world — petabyte-scale, impossible to replicate
- Dedicated on-prem GPU resources — no cloud queues or budget negotiations for compute
- Open-source-first approach with no vendor lock-in
- Grow fast in a small team with direct mentorship from the technical lead
- Competitive salary
- Stock option