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Helsing

Machine Learning Engineer, Detection and Tracking

Helsing is hiring a Machine Learning Engineer, Detection and Tracking in Washington DC, United States. Level rates it ; you can apply on Helsing.

Level's read on this job

  • On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch, MLflow and Weights And Biases among its tools and skills.
  • Level classes it as a senior role.
  • Helsing has 156 other open roles on Level.

AI in this role

Own and train detection and tracking machine learning models for autonomous drone and defense software platforms.

pytorchmlflowweights-and-biasespythontensorrtonnx
fine-tuningml-opscomputer-visionmachine-learningobject-detectionmodel-trainingedge-deployment

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 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
  • U.S. citizenship required

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

Join Helsing and work with world-leading experts in their fields

  • Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns

  • The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world

  • Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defense industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward

  • In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts

  • We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practice responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about

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.

Helsing is an Equal Opportunity Employer. We will consider all qualified applicants without regard to race, color, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, genetics, or any other characteristic protected by applicable federal, state, or local law. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation.

Helsing's Candidate Privacy and Confidentiality Regime can be found here.

How we rate this

Machine Learning Engineer, Detection and Tracking at Helsing rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningML OpsComputer visionMachine learningObject DetectionModel TrainingEdge DeploymentPyTorch

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$200k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  4. Tell me about a project where machine learning was part of your work. What did you do?
  5. Tell me about a project where object detection was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, ML Ops, Computer vision, Machine learning, and Object Detection. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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