Job Opportunities API

[https://app.ashbyhq.com/api/images/user-content/d2dc86a4-ecf9-41be-ae66-8f227e668377/acdffaa9-36b3-4057-ac46-919b2d78c729/Screenshot%202025-08-08%20at%2011.06.06%20AM.png] 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 https://drive.google.com/file/d/1U9z2zG1nQBaMqX3pIMGM1Cn7mjaN3Pc8/view?usp=sharing. 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). - Collabora