SDE III - Engineering Productivity (AI)

Identify and quantify engineering productivity bottlenecks across the full development lifecycle and prioritize solutions to recover engineer time.

Design and build production-quality AI-driven agents, automation, platform capabilities, and process changes to improve engineering throughput, adoption, and measurable cycle time improvement.

Redesign engineering workflows incorporating AI for tasks like code review, test generation, incident diagnosis with guardrails to maintain quality and security, and prove impact through quantitative metrics.

6+ years of professional software development experience at SDE3 level with ownership of production systems.

Proficiency in Python, Java, Go, or equivalent with experience shipping production services and reviewing code.

Strong understanding of distributed systems, APIs, microservices, software architecture, cloud infrastructure, and CI/CD workflows.

Practical experience implementing LLMs and agentic AI systems in real products, and ability to quantitatively measure and influence engineering workflows.

Experienced in tackling org-wide engineering productivity challenges autonomously without needing a given backlog, with ability to influence cross-functional teams without authority.

Capable of building scalable, robust AI and automation systems integrated deeply into engineering lifecycles (e.g., CI, code review, incident response).

Comfortable driving adoption through data-driven experiments and mentoring teams to embrace AI-first engineering workflows aligned to measurable impact.