Clover Health - Staff Technologist

• Own your initiative's outcomes. Take direction from leadership on the problem to solve, then own the product and technical strategy for solving it. • Translate goals into concrete workstreams with clear milestones and metrics that connect your work to business impact. • Drive system design, technology selection, and architecture decisions, including AI-forward systems. Make pragmatic tradeoffs between speed and durability, holding the line on engineering quality while keeping velocity high. • Own delivery personally. You will pull in engineers, data analysts, and other specialists as needed, but you are the one building — not managing the building of — your initiative's systems. • Go deep on the business domain, user needs, operational workflows, and data landscape. Become the person who understands your initiative's problem space better than anyone. • Design experiments, define success criteria, and use data to guide product and technical tradeoffs. Make progress legible to leadership as a byproduct of your methodology. • Deeply understand the business model, technology landscape, and your initiative's strategic context. Build key relationships across engineering, data, operations, and executive leadership. • Own a clear 3–6 month product and technical plan aligned to business objectives. Establish a delivery cadence and ship your first measurable increment. • You have shipped multiple increments with measurable impact on your initiative's core metrics. Progress is visible, incremental, and consistent. • You have established yourself as the single-threaded owner of your initiative. Leadership and operations teams trust your judgment; stakeholders are aligned. • Your initiative has delivered fundamental business value you can quantify and stand behind. • The systems and patterns you've built are durable and well-documented. You've raised the bar for what outcome-driven Staff Technologistership looks like at Clover. • You should get in touch if: • You have strong engineering skills and genuine product instincts. You've operated as a tech lead, staff engineer, engineering manager, or technical cofounder of an early-stage startup. • You have shipped products or programs end-to-end — not just advised on them or managed parts of them. You've owned outcomes, not just deliverables. • You have recent, hands-on experience with AI/LLM systems: building, shipping, or operating AI-backed features, agentic workflows, or ML-driven products. You have opinions about where the field is going. • recent, hands-on experience with AI/LLM systems • You are technically strong: comfortable with system design, distributed systems, data pipelines, and cloud infrastructure. You can hold your own in architecture discussions and make sound technical tradeoffs. • You are fluent in data and have used data and experimentation to make product and engineering decisions. • fluent in data • You thrive in ambiguity. When no playbook exists, you build one. When priorities conflict, you force-rank them. When coordination breaks down, you fix it.