SwarmboticsAI is seeking an MLOps Engineer to design, build, and maintain the machine learning infrastructure for their autonomous swarm systems, specifically the ANTS (Attritable, Networked, Tactical Swarm) product, to address the urgent need for low-cost intelligent autonomous swarm UGV systems in the defense space.
Requirements
- 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)
- Experience with edge AI deployment and embedded systems optimization
Responsibilities
- 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
Other
- Minimum 2 years industry experience in MLOps, DevOps, or ML infrastructure
- 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
- Must be eligible to work on export-controlled projects.
- Ability to relocate to Phoenix, AZ area
