Machine Learning Engineer, Detection and Tracking

EngineersOfAI; Washington (District of Columbia); Hybrid; USD 120,000 - 190,000; Full time

Job summary

Helsing is seeking an applied ML engineer to own the detection and tracking models powering its drone software. You will train, tune, and deploy models against US‑specific datasets, focusing on practical performance and reliability in production rather than theory.

You will manage the full model lifecycle—from data assessment and annotation through training, evaluation, and edge deployment—while improving data pipelines and monitoring metrics to diagnose failures and drive robust, real‑world

Qualifications

• Experience with deep learning for visual detection and tracking.
• Ability to improve model performance on real-world data.
• Familiarity with end-to-end ML pipelines and monitoring processes.

Responsibilities

• Training and fine-tuning detection models (YOLO, DETR, Faster R‑CNN) on mission‑specific datasets.
• Implementing and improving multi-object tracking pipelines (SORT, DeepSORT, ByteTrack).
• Evaluating model performance, diagnosing failures, iterating on data and model improvements.
• Managing the data pipeline end-to-end: data assessment, annotation, curation, augmentation strategies.
• Optimizing models for deployment on SWaP-constrained edge platforms.

Skills

Object detection

Multi-object tracking

Model deployment

Tools

YOLO

DETR

Faster R-CNN

Job description

Who we are

Helsing develops artificial intelligence-enabled capabilities to protect and defend democracies. We build Altra, an AI-powered drone software platform, and HX-2, our autonomous drone. We are growing our US operations, cultivating an ambitious and committed team of mission-driven professionals to apply their skills to solve challenging problems.

The role

You 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