Senior Software Engineer; DevOps, MLOps

Anduril Maritime delivers platforms, systems, and integrated effects in the maritime domain. Our autonomous vehicles (sub-surface and surface) are the cornerstone of these capabilities, and we continually strive to push the boundaries of the possible in terms of endurance, autonomy and mission capability. The Maritime team develops and maintains core products and payloads, and adapts and applies those products to serve a wide variety of defense, IC and commercial customers in US and international markets.

As a Software Engineer, DevOps/MLOps on the Maritime Digital Shipbuilding team, you will build and operate the infrastructure that keeps our digital production systems running at full speed. You’ll develop and manage CI/CD pipelines, automate infrastructure with code, and deploy applications and machine learning models across cloud and edge environments with security, traceability, and reliability in mind.
You’ll work closely with software, data, and operations engineers to turn designs into working systems—streamlining development, improving performance, and keeping production stable as we scale. You’ll also collaborate with digital, manufacturing, and corporate technology teams across Anduril in a high-tech, fast-paced culture of innovation focused on solving real problems and delivering results.
If you’re driven to build systems that last, thrive on deep technical challenges, and want to see your work directly shape how we design, build, and sustain complex platforms, you’ll be helping build the future of digital shipbuilding and the next generation of maritime vehicles.

• Build and Manage CI/CD Pipelines: Develop and maintain CI/CD pipelines using tools like GitHub Actions and Jfrog Artifactory to ensure seamless integration and deployment of machine learning models and applications.
• Infrastructure as Code (IaC): Utilize Terraform and Ansible to automate infrastructure provisioning and management on cloud platforms such as Azure, AWS, or Google Cloud Platform (GCP).
• Containerization and Orchestration: Implement containerization solutions with Docker and manage container orchestration using Kubernetes to ensure reliable deployment and scaling of applications.
• Model Management and Deployment: Set up and maintain model registries and feature stores (e.g., MLflow, Kubeflow), and manage deployment pipelines for both batch and real-time inference.
• Monitoring and Logging: Establish comprehensive monitoring and logging solutions using tools like ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana to ensure the smooth operation of deployment environments.
• Collaborate with Cross-Functional Teams: Work closely with development, data science, and operations teams to foster collaboration and ensure the efficient and effective deployment of machine learning models.
• Optimize Performance: Utilize parallel computing frameworks such as CUDA and OpenCL to accelerate high-performance computing tasks, ensuring timely processing of large datasets and complex simulations.

• Advanced proficiency in programming languages (C++ for high-performance computing, Python for scripting and integration).
• Experience with CI/CD tools like GitHub Actions, Jfrog Artifactory, and Git.
• Proficiency with IaC tools (Terraform, Ansible).
• Experience with cloud platforms (Azure, AWS, GCP).
• Proficiency in containerization (Docker) and container orchestration (Kubernetes).
• Knowledge of model registries and feature stores (e.g., MLflow, Kubeflow).
• Experience with logging and monitoring tools (ELK Stack, Prometheus, Grafana).
• Understanding of parallel computing frameworks (CUDA, OpenCL).
• Strong collaboration skills and proficiency with collaborative tools (JIRA, Confluence).
• Eligible to obtain and maintain an active U.S. Secret security clearance.

• Previous experience in a manufacturing or industrial setting.
• Familiarity with observability concepts and tools.
• Knowledge of security best practices for DevOps and MLOps.