Machine Learning Engineer, Detection and Tracking

EngineersOfAI; Washington (District of Columbia); On-site; USD 90,000 - 130,000; Full time

Job summary

EngineersOfAI in Washington, D.C. is looking for a talented individual to manage the detection and tracking models crucial for our AI products. The ideal candidate will excel in training and optimizing models for real-world applications, primarily focusing on US-specific datasets.

Key responsibilities include training models like YOLO and Faster R-CNN, managing the entire data pipeline, and ensuring model reliability in production. We are searching for innovative thinkers to enhance our mission-driven team.

Qualifications

• Experience with training and fine-tuning machine learning models.
• Strong understanding of model performance and data quality.
• Proficiency with multi-object tracking techniques.

Responsibilities

• Train and fine-tune detection models on mission-specific datasets.
• Implement and improve multi-object tracking pipelines.
• Evaluate model performance and iterate on improvements.
• Manage end-to-end data pipeline for training.
• Optimize models for deployment on constrained platforms.

Skills

Machine Learning

Model Performance Evaluation

Data Quality Assurance

Detection Model Training

Multi-object Tracking

Tools

YOLO

DETR

Faster R-CNN

SORT

DeepSORT

ByteTrack

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 platforms