About Us

ZeroMark builds AI-driven counter-drone systems that actually work in combat. No PowerPoints. No hype. Just field-proven technology that saves lives.

We've doubled year-over-year for two straight years, winning contracts that prove what we've always known: real innovation happens in the dirt, not in conference rooms. Our systems transform standard weapons into AI-powered platforms that detect, track, and neutralize drone threats—because a $200 drone shouldn't require a million-dollar countermeasure.

Here's what makes us different: ZeroMark operators don't build from behind screens. You'll validate tech from Blackhawk helicopters, train alongside Tier-1 units (who happen to be our coworkers), and test at legendary ranges from White Sands to the cliffs of Hawaii. When we say field-tested, we mean you'll shoot it, fly with it, and push it to failure. We don't tweet about changing the world—we're too busy actually doing it. Watch us in action here. Dark humor required, thick skin recommended.

If you want to make an actual impact—and have some unforgettable Tuesday afternoons along the way—let's talk. We're all about delivering practical, field-tested tech, not just theories.

What You'll Do

• Design, develop, and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.

• Collaborate with the general software engineering team to integrate ML models into existing software systems and ensure scalability and maintainability.

• Work in conjunction with computer vision specialists to apply and optimize ML techniques for image and video analysis, object detection, tracking, and recognition in defense contexts.

• Research and evaluate new machine learning algorithms, tools, and technologies to enhance our capabilities and solve challenging problems.

• Perform rigorous model testing, validation, and performance tuning to ensure robustness and accuracy in real-world scenarios.

• Contribute to the development of best practices for ML engineering, including MLOps, version control, and reproducible research.

• Mentor junior engineers and contribute to a culture of continuous learning and knowledge sharing.

• Communicate technical concepts effectively to both technical and non-technical stakeholders.