Applied Machine Learning (ML) Engineer

Location: Remote (Boston or any major California city) or hybrid from Arlington, VA (3 days per week in office) Compensation: $240K–$270K base + equity (targeting $300K+ total comp)flexible for exceptional candidates. About Our Client Our client is a profitable, fast-growing, venture-backed maritime technology company building advanced sensing and intelligence systems for both commercial and defense applications. The growing team is composed of operators and engineers from leading startups and technical organizations, with strong relationships across the Department of Defense and broader national security ecosystem. They are developing edge-intelligent platforms that operate in real-world, high-stakes environments, combining AI, sensor fusion, and distributed systems to deliver actionable insights at scale. The focus is on building systems that work reliably in challenging field conditions, not just in controlled environments. Why This Opportunity This is a chance to work on real-world AI systems deployed in the field. You will build and deploy machine learning systems that operate on edge hardware, integrate across multiple sensor types, and deliver mission-critical insights in real time. The environment is fast-moving, highly technical, and focused on shipping systems that perform under real operational constraints. You will have real ownership, both in terms of early stage equity and in the projects you manage. Role Overview Our client is seeking an Applied Machine Learning Engineer to work across a broad range of perception and machine learning problems powering next-generation sensing systems. This is a highly cross-functional role spanning computer vision, sensor fusion, and real-time inference. You will partner closely with hardware, software, and product teams to take ideas from early concepts through production deployment. The ideal candidate is hands-on, pragmatic, and comfortable working across the full machine learning lifecycle. What You Will Do Design, train, and evaluate models across tasks such as object detection, classification, anomaly detection, and sensor-based inference Optimize models and inference pipelines for edge and embedded environments with compute and bandwidth constraints Build and maintain real-time data processing pipelines across edge and cloud systems Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation Prototype and experiment across computer vision, signal processing, and multi-modal sensor fusion Develop tools for benchmarking, visualization, and debugging model performance Stay current with emerging machine learning techniques and evaluate their applicability to production systems Collaborate across teams and contribute to code reviews and technical documentation What Our Client Is Looking For 4+ years of experience building and deploying machine learning models in production,not solely training or refining models Strong proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow Experience working with diverse data types including images, time-series, geospatial, or RF data Experience deploying machine learning systems in edge or embedded environments Strong understanding of model evaluation, tuning, and monitoring Excellent debugging and problem-solving skills Ability to work cross-functionally and communicate effectively Must be eligible to obtain and maintain a security clearance Nice to Have Advanced degree in Machine Learning, Computer Vision, Robotics, or related field Experience in maritime, aerospace, or remote sensing domains Background in sensor fusion or multi-modal systems