Proactively identify, track, and resolve blockers and dependency risks across engineering, product, design, ML research, data science, SRE, and operations, working independently and in concert with senior product and engineering leadership
Establish and evolve lightweight coordination and delivery infrastructure, including rituals, tooling conventions in Linear, and communication norms, that cultivate strong cross-team alignment
Serve as the key connection between the core LOIS development teams and supporting stakeholder functions (QA, operations, etc.), ensuring timely engagement to enable smooth, rapid feature delivery
Absorb and manage the impact of unexpected changes, delays, and discoveries during development by updating forecasts, communicating tradeoffs, maintaining coordination without losing delivery momentum
Partner closely with product and engineering leadership on release planning, ensuring that program structure reflects and supports strategic priorities
- Build and maintain a culture of transparency and accountability across the program, ensuring that status is honest, risks are surfaced early, and teams have what they need to do their best work
- Qualifications
- 7+ years of technical program management or equivalent experience delivering complex software across multiple geographically distributed teams in different disciplines
Demonstrated ability to hold a large, multi-workstream program together under ambiguity: crafting plans, maintaining them under pressure, and communicating status credibly to both technical and executive audiences
Deep familiarity with software engineering practice at a working level. Strong candidates should be comfortable in conversations about system architecture, APIs, infrastructure, and technical tradeoffs
Proven track record of building coordination processes and infrastructure in environments where they did not previously exist, showing good judgment about deploying formal vs lightweight methods
Strong experience with modern project and issue-tracking tooling (Linear, Jira, or equivalent), and critically the use of these tools to make dependencies and progress visible across a large base of contributors and stakeholders
Experience working on products that incorporate ML or LLM-based components is a meaningful plus, particularly where model behavior, data pipelines, and research cycles intersected with product delivery timelines
Strong communication and stakeholder management skills, with the ability to earn trust and influence outcomes across engineering, product, research, and operations without formal authority over any of them
