Staff Software Engineer

Works across teams to design, build, and operate the infrastructure foundations that power model training, serving, experimentation, and observability for Machine Learning and AI Systems teams. It sits within the ML Platform group in ML Foundations, which builds and maintains infrastructure, deployment tooling, and observability that enable CNN's ML and AI Systems teams to move from prototype to production with velocity and confidence. The role partners with ML engineers, data engineers, and AI Systems engineers to understand production needs, build reliable infrastructure, and deliver tooling that accelerates the team.

• Own end-to-end migration of CNN's ML training and orchestration workloads to Outerbounds-managed Metaflow, ensuring zero production disruption and a clear path to self-service for ML practitioners.
• Establish comprehensive observability across all ML products, AI applications, and shared infrastructure, including the monitoring, alerting, and diagnostic tooling engineers need to operate production systems reliably, and cost attribution that lets us understand and govern ML/AI spend by team
• Product, and use case.
• Design and operate a unified registry and versioning system for ML models and AI applications, providing lineage, reproducibility, and a clean handoff between development and production.
• Evolve the existing feature resolution framework from a loosely coupled set of pipelines into a real platform that lets ML and AI Systems engineers configure content types and events to intercept, register feature-generation APIs
• And land resolved features as durable data products in the feature store, with the resulting ML- and LLM-generated features powering analytics, training, inference, and user-facing rendering.
• Design and own infrastructure, deployment tooling, and developer experience for ML and AI Systems teams, and lead architectural decisions across orchestration, serving, observability, and experimentation infrastructure.
• Build self-service tooling that lets ML practitioners move from prototype to production without platform team dependencies.
• Establish engineering standards for ML/AI infrastructure, including reliability, cost governance, and operational excellence.
• Review designs and code, mentor engineers, and lead cross-team initiatives.
• Partner with Data Platform on infrastructure coordination and data access patterns.
• Communicate effectively across audiences through technical documentation, design reviews, and stakeholder interactions.

• 8+ years building production infrastructure or platform systems, with a Bachelor's degree in Computer Science, Information Technology, or a related technical field (or 6+ years with a Master's degree).
• Deep expertise in distributed systems, with a track record of shipping highly available, low-latency infrastructure.
• Strong proficiency in Python and at least one of Go, Java, or C++.
• Expertise with cloud infrastructure and IaC, especially AWS and Terraform.
• Experience with ML or data infrastructure — orchestration, serving, deployment tooling, observability, or experimentation frameworks.
• Proven track record of leading complex platform projects from concept to production — knowing when to own decisions, when to rally the right people for alignment, and when to escalate.
• Collaborative mindset, understanding that great platform work depends on deep partnership with the teams you serve.
• A passion for helping CNN's engineering organisation grow through mentorship, talent acquisition, and professional development.

• Experience with Metaflow, SageMaker, or comparable ML orchestration platforms.
• Experience with model registries, feature stores, or experimentation frameworks.
• Background in cost governance, FinOps, or multi-tenant infrastructure.
• Practical experience supporting LLM-based or GenAI production systems.
• Prior experience working closely with machine learning engineers.