Machine Learning Engineer

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the next-generation data management and artificial intelligence platform for maritime domain awareness. Northwind Robotics is a growing defense contracting company dedicated to delivering cutting-edge solutions that support our nation's security. As we expand, we're building a culture where innovation meets mission-critical work. We operate with a flat organizational structure that empowers every team member to make an impact, collaborate directly with leadership, and contribute to projects that matter. Whether you're joining our Hardware, Software, or Services division, you'll work alongside talented professionals who are committed to excellence and advancing the capabilities that keep our nation safe and secure. Northwind Robotics builds sonobuoy sensors that are deployed into the water and collect edge data. We also work with the U.S. Navy to collect and process their SONAR data. You'll have an opportunity to work on real-world projects that directly impact warfighter capabilities and mission success. What You'll Do We're a small team wearing many hats, and you'd have a wide variety of responsibilities that include: Design, train, and optimize machine learning models using PyTorch Deploy models to production environments in the cloud and at the edge Build and maintain ML pipelines for training, evaluation, and inference Integrate machine learning models into real-time and batch processing systems Optimize model performance for accuracy, latency, and resource constraints Implement model monitoring, versioning, and deployment strategies Work with signal processing data and time-series analysis Improve local development and CI/CD for ML workflows using modern tooling and GitHub Actions Who You Are We're looking for someone with strong Machine Learning Engineering skills who shares our most important values: You're fanatical about polish. Every detail matters. You love to make sure your code is linted, formatted, fully typed, and has comprehensive test coverage. You care about correctness. You take pride in the fact that your models perform reliably and downstream consumers trust your predictions. You obsess over performance. You daydream about model latency, throughput, and efficient inference pipelines. You dive deep. It's important for you to really know how things work. You're always building prototypes and setting up experiments to reinforce your understanding. You live on the bleeding edge. You've got a long list of upcoming ML techniques and frameworks you're excited about and can't wait to experiment with new approaches. You're a great teacher. You know how to break down complex ML concepts for a specific audience and make it click with them in a way that gets them excited. Why Work With Us We ship — We don't work on 18-month projects that are irrelevant before they're even finished. Our work has impact — We build products that are deployed to U.S. submarines and integrate with the sonobuoys we manufacture. We're growing responsibly — We have the resources to hire a lot more people, but we don't want to build a massive team of people who don't share our values. We're remote — Work from wherever you want. We collaborate in real time on Slack or asynchronously via GitHub. We're profitable — We aren't burning through cash trying to make the business work. But we also have investors who believe in us and are committed to our success. We care about doing great work — You don't need permission to sweat the details here. We don't take ourselves too seriously — We're building products that make the world safer. But we don't let that get to our heads. Important Skills Several years of experience with Python and machine learning frameworks Expertise in PyTorch for building and training neural networks Experience training and serving models in cloud environments (AWS, Azure, GCP) Proficiency with MLOps practices including experiment tracking, model versioning, and deployment Experience with model optimization for production performance and scale Knowledge of Docker and Kubernetes for containerized deployments Familiarity with REST APIs and model serving frameworks Understanding of CI/CD pipelines for ML systems Strong fundamentals in machine learning including model architecture design, training strategies, and evaluation Nice To Have Experience with reinforcement learning algorithms and applications Digital signal processing experience Background in time-series analysis or sensor data processing Experience with edge deployment and model optimization for resource-constrained environments Familiarity with distributed training across multiple GPUs/nodes Experience with model compression techniques (quantization, pruning, distillation) Contributions to open-source ML projects or research publications Experience in defense, aerospace, or other regulated industries What We Offer Unlimited PTO — Take the time you need to recharge and maintain