Principal Software Engineer, ExtraHop

• 8+ years of professional software engineering experience, with 3+ years focused on ML infrastructure, distributed systems, or platform engineering
• Strong programming skills in Python and Go, with an emphasis on writing production-quality, well-tested, and modular code
• Demonstrated experience building and scaling systems that support data-intensive or ML workloads in cloud or hybrid environments
• Solid experience with IaC tools such as Terraform, and configuration management systems (e.g., Ansible, Packer)
• Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes), especially in support of ML workloads
• Working knowledge of ML model lifecycle, MLOps best practices, and ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
• Experience with CI/CD pipelines, automation frameworks, and observability tools (e.g., Prometheus, Grafana, Datadog)
• Excellent problem-solving skills with the ability to lead technically complex projects from concept to production
• U.S. citizenship or lawful permanent resident status required for work in secure environments

Desirable

• As a Principal Software Engineer | MLI at ExtraHop, you’ll work on the design and implementation of scalable systems that support our machine learning projects
• You’ll work closely with data scientists, ML engineers, and cloud teams to create efficient tools and infrastructure for managing machine learning workflows – from data collection and experimentation to deployment and monitoring
• You’ll lead engineering best practices through code reviews, mentorship, and high standards for security, performance, and reliability
• Your experience will help improve our machine learning processes, focusing on clear documentation, easy version management, automated deployments (CI/CD), and secure operations
• Staying informed on new developments in machine learning operations (MLOps), container management, and cloud technologies, you'll help guide decisions to keep our ML platform modern and effective
• Provide technical leadership in the architecture, design, and implementation of robust and scalable infrastructure to support the full lifecycle of machine learning systems—from data ingestion to model deployment and monitoring
• Collaborate with data scientists, ML engineers, and cloud teams to build high-performance services, pipelines, and tooling that accelerate ML experimentation and production workflows
• Drive the evolution of ML infrastructure capabilities, including support for reproducibility, versioning, CI/CD for ML models, observability, and secure deployment
• Design and develop infrastructure-as-code (IaC) solutions using Terraform or similar tools to ensure infrastructure scalability, consistency, and automation
• Champion engineering excellence through code reviews, mentorship, and by setting high standards for performance, security, and maintainability
• Stay informed about industry trends in ML infrastructure, MLOps, container orchestration, and cloud-native tools, and help shape strategic technology decisions
• Contribute to internal documentation, operational runbooks, and design specifications to enable knowledge sharing and operational resilience across the team