The roleYou will own the detection and tracking models that power Helsing's products - training, tuning, and deploying models against US-specific datasets. This is an applied ML role: you won't be writing research papers, but you will be expected to have strong intuition for model performance, data quality, and the practical trade-offs involved in getting detection and tracking systems to work reliably in production. You will manage the full model lifecycle - from assessing and curating training data through annotation, training, evaluation, and deployment to edge platforms.
The day-to-day- Training and fine-tuning detection models (YOLO, DETR, Faster R-CNN, and similar architectures) on mission-specific datasets
- Implementing and improving multi-object tracking pipelines (SORT, DeepSORT, ByteTrack, or similar)
- Evaluating model performance: analyzing metrics, diagnosing failure modes, and iterating on data and model improvements
- Managing the data pipeline end-to-end: assessing raw data, coordinating annotation, curating datasets, and implementing augmentation strategies
- Optimizing models for deployment on SWaP-constrained and embedded platforms (quantization, pruning, TensorRT, ONNX export)
- Collaborating with systems engineers to integrate models into the broader Altra platform
- Working across sensor modalities as needed, including electro-optical, infrared, and other imaging sources
You should apply if you- Have 5+ years of experience in applied machine learning or computer vision
- Have a Bachelor's degree in Computer Science, Electrical Engineering, or a related field; Master's or PhD strongly preferred
- Have production experience training and deploying object detection models - not just research or academic projects
- Are proficient in Python and PyTorch or a comparable deep learning framework
- Have strong intuition for data quality; you can look at annotated datasets, training curves, and evaluation metrics and know what's wrong
- Have experience with the full model training lifecycle: data curation, annotation management, training, evaluation, and deployment
- Have experience optimizing models for deployment on SWaP-constrained and edge platforms (TensorRT, ONNX, quantization)
- Understand multi-object tracking and have implemented or worked with tracking algorithms in practice
- Can read and contextualize scientific papers in computer vision and apply findings to production systems
- Are a U.S. citizen with an active security clearance or the ability to obtain one
Nice to have- Strong proficiency in Rust or C++ for production model deployment and optimization
- Experience with multiple sensor modalities - particularly infrared or thermal imaging
- Familiarity with MLOps tooling: experiment tracking (MLflow, Weights & Biases), dataset versioning, model registries
- Experience with annotation tools and workflows (CVAT, Label Studio, or similar)
- Background in computer vision beyond detection - segmentation, pose estimation, activity recognition
- Experience with simulators, emulators, or synthetic data generation for training and evaluation
- Experience deploying models on GPU-accelerated embedded platforms (NVIDIA Jetson, similar)
- Background in defense, intelligence, or other mission-critical environments
What we offer- A focus on outcomes, not time-tracking
- A generous compensation and benefits package (in addition to base salary) that includes, but may not be limited to, insurance coverage (medical and travel), flexible paid time off, paid holidays, and remote and/or hybrid work available depending on position. All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated or superseded from time to time.
