Lead Computer Vision & ML Engineer — Founding Role
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 Lead CV/ML Engineer as a founding member of the engineering team. This is not a role where you inherit a codebase and maintain it. You will define the ML architecture, choose the tools, build the pipeline from scratch, and grow a team around it as the platform matures.
The technical plan outlines the direction — petabyte-scale video processing, active learning, edge deployment on Jetson — but the specific implementation decisions are yours to make. We value strong technical judgement over checkbox experience with a predefined stack.
What You Will Own
ML Architecture & Technical Direction
- Define the end-to-end ML pipeline: from raw video ingestion through data curation, training, and deployment to UAVs
- Evaluate and select the tools and frameworks that best fit the problem (our current thinking: PyTorch, Ray, MLflow, FiftyOne — but nothing is set in stone)
- Establish training infrastructure, experiment tracking, and model versioning from day one
- Make build-vs-integrate decisions across the stack
Data Pipeline & Curation
- Design video preprocessing pipelines: clip segmentation, frame extraction, scene detection, embedding extraction
- Build a data curation workflow to select diverse, high-quality training subsets from a massive raw corpus
- Define the annotation ontology and labelling policy for detection, classification, and multi-object tracking
Model Development
- Train and iterate on computer vision models for object detection, scene classification, tracking, and segmentation
- Implement distributed multi-GPU training for large-scale experiments
- Design the active learning loop: auto-labelling with confidence routing, human review integration, retrain triggers
- Define quality control metrics and model promotion criteria
Edge Deployment
- Optimize models for real-time inference on NVIDIA Jetson (ONNX, TensorRT, INT8 quantization)
- Validate edge accuracy against training baselines
- Design the model delivery pipeline from registry to deployed UAVs
Team Building
- Hire and mentor ML engineers as the team grows
- Establish engineering practices: code review, testing, documentation
- Own the ML technical roadmap and communicate it across the company
What We Are Looking For
Must Have
- 5+ years in computer vision and deep learning, with production deployments
- Strong PyTorch proficiency, including distributed training
- Experience with object detection and tracking pipelines (YOLO family, RT-DETR, Detectron2, or similar)
- Hands-on model optimization for edge or mobile deployment (TensorRT, ONNX, SNPE, or similar)
- Experience building training infrastructure (pipelines, experiment tracking, CI/CD for ML)
- Proven ability to lead a small engineering team (hiring, mentoring, technical guidance)
- Python as primary language; clean, production-grade code
Strong Plus
- UAV / drone / robotics domain experience
- NVIDIA Jetson deployment experience
- High-performance video processing pipelines (DeepStream or custom)
- MilTech or defense-related work
- Experience defining ML architecture from scratch at an early-stage company
- C++ for performance-critical components
Proposed Tech Stack
This is our starting point, not a mandate. The right candidate will validate, adjust, or replace these choices based on their experience.
- 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
- A founding engineering role with direct influence on the technical direction of the company
- Ownership of the full ML pipeline end-to-end — from raw video to models flying on UAVs
- 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
- The opportunity to build and lead an ML team from the ground up
- Competitive salary
- Stock option
