Manager, Engineering

Posted: 2026-09-11

Delivery ● Lead sprint delivery for a team of 6–12 engineers: planning, dependencies, risks, release readiness. ● Think about how work gets sequenced across people and AI-assisted workflows, not just story points. ● Keep JIRA, ProductBoard, and the dependency tracker current. No stale tickets, no surprises at SoS. ● Track cycle time and escaped defects; own the trend, not just the number.

Technical ● Review code and AI-generated output for whether it solves the right problem. Clean tests aren't enough. ● Define what needs human sign-off, what can be spot-checked, and what warrants a second pass. ● Write specs that work as contracts: clear scope, explicit constraints, stated boundaries. ● Stay close to architecture decisions, tech debt, and incident response. ● Contribute to software architecture decisions and patent writing for innovative work.

People ● Run 1:1s that are about growth, not sprint status. ● Coach engineers on when to trust AI output and when to push back. Model it explicitly, not just in retrospect. ● Give clear, timely feedback; build real development plans; address performance early. ● Help your team see that directing AI well is a skill worth building, not just a tool to adopt. ● Calibrate hiring panels, provide written feedback, contribute to calibration discussions.

Cross-functional & Culture ● Be the PM partner who holds a definition-of-ready and surfaces risks before they land in a leadership meeting. ● Engage escalations directly; produce RCAs; own P1/P2 support SLAs. ● Set the tone on your team: accountability, directness, recognition of others' work. ● Watch for workload and wellbeing signals before they become problems. ● Lead by influence across teams and functions ; comfortable driving cross-team technical decisions without direct authority

● 10+ years in software engineering, including 2+ years managing teams of 5 or more. ● BS/MS in Computer Science, Software Engineering, or a related field. ● Enough architectural depth to evaluate tradeoffs, including in AI-assisted output. ● Background in platform, API, data, or infrastructure engineering. ● Comfortable in a PM/Design pod environment; fluent with JIRA and ProductBoard or equivalent. ● Strong fundamentals in data structures, design patterns, and object-oriented programming. ● Proficient in Python or Java; enough depth to engage meaningfully in code review and design conversations. ● Strong enough in distributed systems, data pipelines, or APIs