• Lead customer-facing Agentic AI product leveraging LLMs, agents, and internal APIs. • Work with partners like OpenAI to integrate Jerry.ai services into the ChatGPT app. • Define prompt consolidation and migration strategy. • Own the technical prioritization queue and chatbot prioritization. • Track emerging technology and manage the OpenAI relationship.

• Lead the customer-facing Agentic AI product leveraging LLMs, agents, and internal APIs. • Define the strategy for migrating prompts onto a unified platform. • Own the technical prioritization queue for chatbot projects. • Track voicebot and computer use for investment decisions. • Collaborate with engineering, data, operations, and other teams on execution.

• Define and continuously refine the operating model for the non-linear productivity pilot: what "friction" means, how it's diagnosed, and how fixes are prioritized against the three system-level metrics. • Identify and prioritize non-linear productivity opportunities (step-change fixes rather than incremental gains) across the SDLC, including flaky tests, review latency, pipeline reliability, tooling gaps, and context loss for both human and agentic contributors. • Translate an intentionally broad, ambiguous mandate ("find the friction, fix it at the root") into a concrete, time-boxed roadmap with defined milestones for a 3-month pilot window. • Personally source, evaluate, and select 4 exceptional engineers for the pilot squad. You own this hiring bar directly rather than delegating it to a pipeline. • Negotiate allocation models (100% vs. partial/dual-hat) with each engineer's current EM, balancing team commitments against the pilot's needs. • Set the technical direction and working norms for a small, fast-moving team: how work is scoped, how decisions get made, and how progress gets reported. • Mentor the engineers on this team in root-cause diagnosis and shift-left thinking, raising the bar for rigor versus reaching for the nearest patch. • Lead hands-on investigation to instrument and baseline the three top-level metrics (MRPM, pipeline success rate, pipeline latency) so the org has an honest starting point before any fix is proposed. • Diagnose the highest-leverage friction points dragging each metric down, and validate hypotheses with real data before committing team time to a fix. • Design and implement fixes that compound, including shift-left quality changes, tooling investment, and better context and interfaces for both humans and agents, rather than one-off patches. • Stay hands-on enough to review architecture, dig into pipeline internals, and unblock the team on hard technical problems directly. • Own a small set of high-impact friction areas as pathfinders, driving each from diagnosis through fix to measurable metric movement. • Work directly with affected engineering teams to ensure fixes are trusted, adopted, and don't quietly get reverted or worked around. • Track and report metric movement honestly, including when a bet didn't pay off. • Capture and codify reusable patterns and playbooks so that fixes validated by this team can be adopted org-wide with minimal friction. • Set a cadence for reporting impact back to EM peers and leadership, framed around metric movement rather than activity. • Ensure the pilot's fixes degrade gracefully and don't introduce new reliability risk into CI/CD systems that other teams depend on.

• Build and lead a team with deep platform expertise in Git and Gitaly, hiring and developing engineers who can operate this infrastructure layer with strong operational judgment. • Own production reliability for Git and Gitaly across GitLab's full deployment footprint. • Drive incident management, root cause investigation, and postmortems with the technical depth to distinguish a Git internals problem from a generic infrastructure problem. • Design and operate Git and Gitaly service models that work across Self-Managed, GitLab Dedicated, and SaaS, with a clear view of which elements are common and which must differ between them. • Maintain cloud-agnostic architecture and tooling decisions, avoiding designs that assume a single hyperscaler's primitives. • Partner with the teams that own each deployment model so operational practices stay coherent rather than fragmenting into separate playbooks. • Apply distributed systems fundamentals, including consistency, replication, and failure domains, to Gitaly's storage and replication model. • Own elastic storage and compute strategy for Git repository data, including capacity planning, autoscaling, and consumption-based provisioning for GitLab's largest self-managed and SaaS customers.