Machine Learning Operations Engineer

Join to apply for the Machine Learning Operations Engineer role at Swarmbotics AICompany BackgroundSwarmboticsAI is pushing the frontier of advanced machine learning models and architectures on edge devices for swarms of unmanned ground vehicles (UGV). We see an urgent need for low-cost intelligent autonomous swarm UGV systems in the defense space. Our primary product is a defense application of swarm UGVs, collectively termed - Attritable, Networked, Tactical Swarm (ANTS). Each UGV in ANTS is an independently-tasked, attritable robot designed for on-demand and autonomous mobility. When operating as a swarm, ANTS is capable of executing advanced and coordinated high-level capabilities across multiple domains.Stephen Houghton and Drew Watson are the Founders and have decades of experience in self-driving cars and trucks, humanoids, and UAVs with experience from NASA, JPL, Cruise, Embark, McKinsey, Amazon, and the CIA.Position DescriptionSwarmboticsAI is seeking a highly skilled MLOps Engineer to design, build, and maintain the machine learning infrastructure that powers our autonomous swarm systems. This engineer will be responsible for creating robust, scalable ML pipelines that support our perception team's cutting-edge computer vision and deep learning models. You'll ensure seamless model training, deployment, and monitoring across our fleet of UGVs. This engineer will work closely with our ML/Perception team and company leadership to scale our ML capabilities across the SwarmboticsAI product roadmap.What You'll DoDesign and implement end-to-end ML pipelines for training, validation, and deployment of perception modelsDevelop robust data management systems for large-scale sensor data (cameras, LiDAR, IMU) collected from field operationsImplement model monitoring, A/B testing, and performance tracking systems for deployed modelsBuild CI/CD pipelines for model versioning, testing, and deployment to vehicle fleetsDesign distributed computing solutions for large-scale data processing and model trainingCreate tools for data annotation, model evaluation, and performance visualizationWork collaboratively with perception engineers, robotics teams, and field operationsRequired QualificationsMinimum 2 years industry experience in MLOps, DevOps, or ML infrastructureBachelor's degree in computer science, engineering, or related fieldStrong experience with ML pipeline orchestration tools (Kubeflow, MLflow, or similar)Proficiency in containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure)Strong Python programming and Linux system administration skillsExperience with model serving frameworks (TensorRT, ONNX Runtime, TorchServe)Knowledge of data versioning and experiment tracking (Weights & Biases, Neptune, or similar)Experience with monitoring and logging systems (Prometheus, Grafana, ELK stack)Strong organization and communication to work well across teams in a fast-paced startup environmentComfort working in the high-paced, fluid environment of a tech startupExcitement about contributing to the defense of the United States and its alliesMust be eligible to work on export-controlled projects.Ability to relocate to Phoenix, AZ areaNice To Have QualificationsMasters degree in computer science, engineering, or related fieldExperience with edge AI deployment and embedded systems optimizationPrior robotics or autonomous vehicle MLOps experienceExperience with real-time data streaming (Kafka, RabbitMQ)Knowledge of security and compliance requirements for defense applicationsExperience with multi-modal sensor data processing and fusionFamiliarity with ROS and robotics software stacksSwarmbotics is an equal-opportunity employer. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, caste, creed, religion, sex, gender identity, sexual orientation, national origin, ancestry, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law. #J-18808-Ljbffr

Created: 2025-10-08