Note: The job is a remote job and is open to candidates in USA. Anno.ai is a defense technology startup developing and testing next-generation autonomous systems. The Senior Machine Learning Engineer will operationalize production machine learning and statistical modeling software, build scalable MLOps pipelines, and collaborate with autonomy, software, systems, hardware, and field teams to deliver secure, mission-ready ML capabilities.
Responsibilities
- 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
Skills
- 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
- * 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%
- * Master's preferred
- * 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)
Benefits
- 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
- Remote work arrangement
Company Overview