Senior Machine Learning Engineer

What We Do at Anno.ai

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 are designed to 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, not only guide how we work internally, but also how we partner with customers and stakeholders.

At Anno.ai, 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.

Disclaimer:  Due to the sensitive nature of our engineering work, Anno.ai enforces strict digital footprint and identity verification. We actively monitor for synthetic profiles, proxy networks, and AI interview assistants; any fraudulent activity will result in immediate disqualification. 

Position Overview  

As a Senior Machine Learning Engineer at Anno.ai, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled 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. You will join a team of beasts known as “Annomals” are notable for their practical, mission-driven, and fun demeanor. MLEs work directly with product, user-facing, hardware, and platform teams to deliver the highest quality products, and because of these diverse interfaces, we valuegood, seasoned judgment in your approach to management, your career growth, and maintaining ethicaland responsible practices.  

For this opportunity we are looking for MLEs who have a fairly uniform distribution of talent across a breadth the range of machine learning tasks and skills. You are an experienced MLE, part solid software engineer, and part modeling expert. You have been through the trenches and bring key knowledge and intuition through your combination of training and experience.  

Candidates need to be able to obtain and maintain U.S. Government security clearance (U.S. citizenship required).  Candidates must be able to travel up to 20% of the time.  

What You Will Do  

• Operationalize machine learning models by building and maintaining 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 up to date models and associated data pipelines
• Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and incorporating 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, extensibility, scalability, and deployment speed of ML systems 

Required Qualifications  

• 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, BentoML)
• 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 (e.g., Git, Code Review, Metrics Evaluation)
• Familiarity with monitoring, observability, and logging platforms (e.g., Prometheus, Grafana, ELK/EFK)
• Ability to obtain and maintain U.S. Government security clearance (U.S. Citizenship required)
• Ability to travel up to 20% 

Preferred Qualifications  

• Experience with deploying models and associated runtimes to Edged Devices
• Experience optimizing models for memory and CPU constrained systems (e.g., embedded systems, microcontrollers)
• Prior experience supporting U.S. Department of War programs, cUAS systems, or mission-critical autonomous platforms
• Experience working with diverse or atypical data sources (e.g., Audio/Acoustics, RF signals, EO/IR imagery)
• Experience deploying and optimizing ML inference on edge or resource-limited compute systems
• Experience with Explainable/Auditable AI/ML tools and interpretable model design
• Experience with AI Software Development Tools (e.g., GitHub CoPilot, Claude) 

Total Rewards Package for Our US Employees 

• Competitive salary
• Equity
• Comprehensive benefits package
• 401k with a 5% company match
• Paid holidays and generous paid time off offering
• Paid leave programs
• Patent bonus program
• Employee referral bonus program
• Learning and development program
• Opportunity to work with a team of highly skilled, creative and motivated team members

Quick Note on Role Fit

If you think you have what it takes to fulfill this opportunity, but don't necessarily check every box, please still connect with us at  [email protected]. Feel free to send a cover letter so we can get to know you better!