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Job Description

We are looking for a Sr. Staff Engineer to define how AI changes the way our engineering organization builds software. This is a hands-on technical leadership role for someone with deep expertise in AI-assisted development who knows how to scale it responsibly across a large engineering org and who can strengthen the engineering foundation required to do it well.

You will own our AI adoption strategy end-to-end: evaluating and selecting tooling, designing integration patterns, building internal playbooks, and working directly with engineers to embed AI into the development lifecycle in ways that produce real, measurable outcomes. Engineering excellence (standards, architecture, process) is the platform you will build to make that adoption stick.

Key Responsibilities

AI Strategy and Adoption

• Own the org-wide strategy for AI in the software development lifecycle: where to apply it, how to evaluate it, and how to scale what works.

• Pilot and operationalize AI tooling across the SDLC, including AI pair programming, LLM-assisted code review, automated test generation, intelligent observability, and agentic development workflows.

• Define adoption frameworks that account for productivity, code quality, security, cost, and responsible use, not just rollout logistics.

• Establish metrics that measure AI's actual impact on engineering velocity, quality, and developer experience, and report findings to engineering leadership.

• Bring well-reasoned tooling recommendations to engineering leadership, not just a summary of what exists in the market.

• Align engineering AI adoption with the broader organizational AI journey, including approved vendors, enterprise policies, and coordination with Security and Legal.

AI System Design and Technical Depth

• Provide architectural guidance for engineering teams building systems that integrate LLMs, AI agents, or ML models into production software.

• Define engineering standards for AI-integrated development tooling: prompt engineering practices, evaluation frameworks, latency and cost tradeoffs, and observability for AI tools where outputs may vary.

• Work hands-on with engineers on hard problems, including reviewing AI-integrated system designs, writing reference implementations, and unblocking adoption at the code level.

Engineering Excellence as an Enabler

• Identify and close the engineering gaps that slow AI adoption: insufficient test coverage, brittle CI/CD pipelines, poor observability, and unclear code ownership.

• Define and strengthen the engineering standards that make AI tooling more effective, treating this as a prerequisite to scaling AI well rather than a separate initiative.

• Drive architectural consistency acrossteams,so AI-generated code and AI-assisted workflows do not introduce new forms of technical debt.

Technical Leadership and Influence

• Serve as the primary technical authority on AI-assisted development across the engineering organization.

• Partner with engineering leads to embed AI practices into team workflows, onboarding, and code review culture as a sustained capability, not a one-time workshop.

• Influence engineering roadmaps and toolchain decisions at the director and VP level through clear, evidence-based technical recommendations.

• Produce internal technical references (ADRs, integration guides, evaluation scorecards) that teams can act on independently.

Qualifications

Required:

• 10+ years of software engineering experience, with 3+ years focused on AI/ML tooling, LLM integration, or AI-assisted development workflows.

• Hands-on, production-level experience with AI developer tools such as GitHub Copilot, Cursor, or LLM-powered code review and test generation.

• Deep understanding of LLM fundamentals: prompt engineering, context management, fine-tuning tradeoffs, and evaluation of AI tools used in the development workflow.

• Experience designing and shipping AI-integrated systems at scale, with a clear understanding of cost, latency, and quality tradeoffs.

• Demonstrated ability to drive technical adoption across large engineering organizations without direct authority.

• Strong written and verbal communication skills with the ability to make AI technical tradeoffs legible to both engineers and senior leaders.

Preferred:

• Prior experience in a principal or staff-level IC role with cross-org scope.

• Experience building internal AI enablement programs, developer experience platforms, or AI governance frameworks.

• Contributions to AI/MLopen-sourceprojects or technical writing on AI engineering topics.

• Background in platform engineering or developer tooling with an AI focus.

Why Join Us?

• Be the primary technical voice shaping how AI transforms software delivery across a large engineering organization.

• Work at the intersection of cutting-edge AI tooling and real engineering systems, with the scope and autonomy to drive both strategy and execution.

• Leave a lasting impact on how hundreds of engineers build and ship software every day.