Own the reliability, performance, and operational health of production AI services
Refactor and harden existing systems to improve resilience, clarity, and maintainability
Diagnose and resolve issues across distributed services, data pipelines, and storage layers
Design and implement monitoring, alerting, and debugging tools for high-availability systems
Partner with researchers and engineers to productionize predictive systems at scale
Establish best practices for testing, deployment, capacity planning, and incident response
Contribute to incident response and postmortems, driving continuous improvement

Ability to travel to our office in Santa Barbara, CA, a few times per year
7+ years of professional software engineering experience
Strong proficiency in Python and at least one JVM language (Java, Scala, Kotlin)
Proven experience designing, building, and operating distributed systems in production
Strong understanding of service architecture, concurrency, resource management, and distributed failure modes
Experience operating Kubernetes deployments
Strong experience with relational databases, including query performance analysis, indexing, and connection management
Demonstrated ability to diagnose and resolve performance, scalability, and reliability issues across system layers
Experience implementing automated testing and production observability (logging, metrics, tracing)
Experience collaborating with ML or data science teams (deep ML expertise is not required)
Ability to improve system architecture and engineering practices through design, code review, and mentorship

Senior Software Engineer – AI Reliability

Key skills