Staff Software Engineer

Description:

We're looking for a Staff-level Software Engineer who is as comfortable designing production systems as they are applying AI to real business problems. This is a hybrid role, not a pure AI research or ML engineering position. You'll build the internal tools and platforms that power our operations teams, and be the technical expert on where AI creates real value and where it doesn't.

You'll set technical direction across multiple teams and systems, not just the ones you build yourself. When the business needs to explore an AI opportunity, weigh technology options, or understand where we stand on AI, you'll investigate, recommend, and deliver. You'll navigate ambiguity and make well-reasoned build-versus-buy decisions, then drive the recommended solution through implementation. You'll also think about the data each product generates, so what we build feeds our analytics and helps leadership decide where to invest.

You bring strong engineering judgment, a bias toward pragmatic solutions, and the ability to turn open-ended questions into decisions and working software. You'll be a trusted technical advisor to leadership, and your impact will be measured by what the whole team ships, not just what you build yourself.

What You Will Own

Architecture & Delivery

Build and own. Architect and build internal tools and platforms for operations teams, owning them from design through deployment and ongoing operation, including reliability and incident follow-through.
Set direction. Define service boundaries, integration patterns, and data flow across teams, and write the technical strategy and RFCs that make the direction clear.
Simplify. Reduce operational toil by retiring or consolidating systems, managing tech debt and migrations, and weighing long-term maintenance cost in every build-versus-buy decision.
Integrate and protect. Build reliable integrations with legacy and third-party SaaS systems, with security, access control, and audit trails designed in for sensitive data.

Applied AI

Apply AI with judgment. Identify where AI belongs in a solution and where it doesn't, and design it as a well-engineered part of the system (retrieval, agents, LLM-powered features, workflow automation).
Prove it, then ship it. Run time-boxed proofs of concept that answer "is this worth doing?" with evidence, then advance successful proofs of concept into production-ready solutions, with evaluation, observability, reliability, and security.
Guide AI investment. Assess our AI maturity, define a practical roadmap, and track AI cost and ROI so investment decisions rest on data.

Data

Build products that produce good data. Design data models and instrumentation so every product generates clean, usable data for analytics and strategic planning.

Technical Leadership

Lead across competing priorities. Align teams with differing priorities on shared platforms, standards, and tradeoffs. Build consensus through influence and evidence, and drive decisions when consensus cannot be reached.
Improve engineering quality and delivery practices. Write high-quality code and lead through code reviews, design reviews, and engineering standards, using AI coding tools (e.g., Claude Code, GitHub Copilot) to raise the whole team's delivery speed.
Mentor and advise. Mentor engineers and serve as a trusted technical advisor to stakeholders and leadership, explaining tradeoffs clearly and driving decisions to closure.

Required

What You Bring

10+ years of professional software engineering experience, with a track record of designing and shipping reliable, maintainable production systems.
Demonstrated Staff or Principal-level scope: you have led architecture or a major technical initiative across multiple teams with competing priorities, and aligned them on shared platforms or standards.
Hands-on experience building applications with LLMs and generative AI in production, not just experiments. Demonstrate experience operating AI systems at scale, addressing production challenges, and measuring outcomes.
Working experience rolling out agentic systems into production, including evaluation, guardrails, and monitoring.
Strong backend and systems design skills, including APIs, data modeling, cloud infrastructure, and integration with legacy and third-party SaaS systems.
Reliability engineering experience, including SLOs, idempotency, retries, partial failure handling, and data consistency across integrated systems.
Experience building systems that handle sensitive data, with sound security and governance practices such as access control, audit trails, and PII and privileged data handling.
A track record of taking ownership of essential engineering work: tech debt strategy, migrations, and deprecation of systems that are no longer worth their cost.
Experience designing data models and instrumentation that support analytics.
Working knowledge of modern AI application patterns: RAG, embeddings, vector search, prompt, harness, and context engineering, tool use and agents, and evaluation methods.
Ability to weigh build-versus-buy tradeoffs and balance technical options against cost, complexity, and business value, including choosing simpler solutions or stopping projects when the evidence doesn't support them.
Strong communication skills, including explaining AI concepts and tradeoffs to non-technical audiences and driving decisions to closure.