About the role
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. What You'll Need Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field. Experience: 5+ years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments. Technical Skills: Strong proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Solid understanding of core machine learning concepts, including supervised, unsupervised, and reinforcement learning. Experience with various machine learning model architectures and their application (e.g., CNNs, RNNs, Transformers, decision trees, support vector machines). Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). Experience with MLOps tools and practices. Experience deploying a variety of edge systems. Experience with TensorRT and other similar technologies. Deep knowledge of C++ and Python. Domain Knowledge: Experience or strong interest in defense, aerospace, or related industries is highly desirable. Understanding of the unique challenges and considerations for deploying ML in defense applications (e.g., adversarial robustness, real-time constraints, data security). Collaboration & Communication: Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams. Ability to translate complex technical concepts into clear and concise language. Problem-Solving: Strong analytical and problem-solving skills, with a proactive and innovative approach. Ability to work independently and manage multiple priorities in a fast-paced environment.
