Anno.ai is focused on accelerating the safe and effective development of next-generation autonomous systems by building and operating advanced test ranges for low Technology Readiness Level (TRL) autonomous platforms. The company aims to bridge early-stage innovation with real-world mission requirements by testing emerging autonomous technologies for resilience, adaptability, and operational relevance.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical field (Master’s preferred)
- 5+ years of professional experience in software engineering, machine learning engineering, MLOps, or related roles
- Experience operationalizing ML systems at production scale, including model training, versioning, packaging, deployment, and monitoring
- Strong proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow)
- Hands-on experience with MLOps frameworks and workflow tooling (e.g., MLflow, Kubeflow, Airflow, DVC)
- Experience deploying containerized ML services using Docker and orchestrating workloads using Kubernetes (including air-gapped or constrained deployments)
- Understanding of CI/CD workflows and DevOps practices applied to ML systems
Responsibilities
- Operationalize machine learning models by building robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-prem, and edge compute environments
- Work closely with autonomy researchers, software engineers, systems teams, and field operators to translate mission requirements into deployable ML capabilities
- Implement automated CI/CD workflows tailored to ML systems, ensuring repeatable experiments, reliable packaging, and continuous delivery of both models and data pipelines
- Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and model serving platforms (e.g., Seldon, KServe, BentoML)
- Develop monitoring systems to track model health, performance, data drift, system utilization, and mission relevance using tools such as Prometheus, Grafana, and ELK/EFK stacks
- Ensure ML deployments meet defense, customer, and platform security requirements, with emphasis on data integrity, traceability, and operational reliability
- Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to enhance reproducibility, scalability, and deployment speed of ML systems
Other
- The ideal candidate for this role would reside in Minnesota
- Candidates need to be able to obtain and maintain U.S. Government security clearance (U.S. citizenship required)
- Ability to travel up to 20% of the time
- Master’s degree preferred
- Communication and cross-functional collaboration experiences are implied through the role description
