Senior Machine Learning Engineer

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Anno.ai is a mission-focused defense technology startup dedicated to accelerating the safe and effective development of next-generation autonomous systems. We specialize in building and operating advanced test ranges for low Technology Readiness Level (TRL) single- or dual‑use autonomous platforms, providing a critical bridge between early-stage innovation and real-world mission requirements.

Our ranges replicate complex, contested, and dynamic environments—giving innovators, researchers, and defense partners the ability to validate, stress‑test, and mature their systems with speed and rigor. By combining deep technical expertise with a strong national security ethos, Anno.ai ensures that emerging autonomous technologies are tested not only for performance, but for resilience, adaptability, and operational relevance.

Anno.ai is a growing company with a team drawn from diverse professional backgrounds, bringing together expertise in defense, technology, engineering, and operations. We intentionally build our teams on the foundation of trust. Our values—including the trust rule, ownership, bias for action, never stop learning, and sustainable excellence—guide how we work internally and how we partner with customers and stakeholders.

, we believe that mission success depends on empowering innovation at the edge. We exist to help our partners move faster, fail smarter, and ultimately deliver autonomous capabilities that safeguard both national security and the future of global stability.

As a Senior Machine Learning Engineer , you will design, develop, test, document, deploy, and maintain production machine learning and statistical software to automate processes and streamline our customer's mission operations. MLEs work directly with product, user-facing, hardware, and platform teams to deliver the highest quality products. Annomals are practical, mission‑driven, and fun. We value good management, your career growth, and ethical, responsible practices.

For this opportunity we are looking for MLEs who have a fairly uniform distribution of talent across the range of machine learning tasks and skills. You are an experienced MLE, part solid software engineer, part modeling expert.

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). The company would pay for clearance costs. They also need to be able to travel up to 20% of the time.

• 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, Bento
  
  ML)
• 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

• 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,…