Machine Learning Operations Engineer

Company Background:

SwarmboticsAI 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 description:

SwarmboticsAI 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 do:

• Design and implement end-to-end ML pipelines for training, validation, and deployment of perception models
• Develop robust data management systems for large-scale sensor data (cameras, LiDAR, IMU) collected from field operations
• Implement model monitoring, A/B testing, and performance tracking systems for deployed models
• Build CI/CD pipelines for model versioning, testing, and deployment to vehicle fleets
• Design distributed computing solutions for large-scale data processing and model training
• Create tools for data annotation, model evaluation, and performance visualization
• Work collaboratively with perception engineers, robotics teams, and field operations

Required qualifications:

• Minimum 2 years industry experience in MLOps, DevOps, or ML infrastructure
• Bachelor's degree in computer science, engineering, or related field
• Strong 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 skills
• Experience 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 environment
• Comfort working in the high-paced, fluid environment of a tech startup
• Excitement about contributing to the defense of the United States and its allies
• Ability to relocate to Phoenix, AZ area

Nice to have qualifications:

• Masters degree in computer science, engineering, or related field
• Experience with edge AI deployment and embedded systems optimization
• Prior robotics or autonomous vehicle MLOps experience
• Experience with real-time data streaming (Kafka, RabbitMQ)
• Knowledge of security and compliance requirements for defense applications
• Experience with multi-modal sensor data processing and fusion
• Familiarity with ROS and robotics software stacks

The preceding description is not designed to be a complete list of all duties and responsibilities required for the position. Swarmbotics 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.