About the role

Staff Engineer, GTM AI

The Role

We are seeking a Staff Engineer to serve as the senior technical leader responsible for defining and executing the long-term platform strategy for the AI systems that power our go-to-market organization. This role will directly shape how we use data and AI to improve customer and account health visibility, and drive long-term customer value through increased engagement, adoption, and retention.

You will work from a clear vision of the desired future state and methodically guide systems toward that outcome through deliberate architectural decisions and pragmatic, iterative execution. As the company accelerates investment in AI, you will play a pivotal role in defining how AI capabilities are designed, governed, scaled, and embedded into core GTM workflows in a secure, reliable, and cost-effective manner.

You'll set the highest engineering standards and act as the strategic technical guide for the team — ensuring that rapid innovation in the application pods never compromises the long-term integrity of the core platform. This is the role that ensures we can scale from one track to three without breaking what already works.

Responsibilities

Architectural Ownership: Serve as the technical authority for the AI platform, owning architectural direction and long-term system health across multiple business functions. Translate strategic business priorities into scalable architectures that support sustained growth and enterprise adoption.
Platform Evolution: Define and evolve the architecture of our data and AI platform — including data processing pipelines, integration layers, and the services that power real-time account intelligence.
AI Governance: Define standards for AI service governance, evaluation, observability, performance, and cost management. Ensure AI capabilities are production-grade, auditable, and trustworthy.
Technical Leadership: Act as a hands-on technical leader — contributing high-leverage code, mentoring, and raising execution standards across the organization. Guide engineers so that rapid prototyping is built on a sound, scalable foundation.
Data Integrity: Own the "Golden Record" strategy — the system of record for account intelligence across the full revenue lifecycle. Establish durable data modeling standards to ensure consistency and long-term maintainability.
Risk Anticipation: Anticipate structural and scaling risks, and guide systems toward the desired future state through deliberate, iterative modernization — before problems become visible to users.

Engineering Outcomes You'll Own

Scalability: The platform grows from one use case to many without regressions, outages, or loss of data fidelity. New capabilities ship on top of a foundation that holds.
Earned Trust: Users and leadership trust the AI's outputs because the underlying systems are provably accurate, auditable, and consistent. Trust is an engineering outcome, not a marketing one.
Team Leverage: The architecture enables engineers with less context to build and ship safely. Good abstractions, clear boundaries, and well-designed interfaces multiply the output of every person on the team.
Sustainable Velocity: Rapid prototyping in the pods doesn't accumulate hidden costs. The platform absorbs complexity so the rest of the team can stay fast.

Must Have Requirements

6–8+ years of software engineering experience, with a proven track record of owning complex system architectures.
Strong hands-on engineering focus (~90% hands-on, 10–20% solution design).
Prior experience building distributed systems and pipelines from scratch using object-oriented programming.
5–10 years of experience in Python, web application development, and data engineering.
Deep expertise in building scalable, enterprise-grade platforms that process large volumes of data securely.
Strong focus on system reliability, observability, and engineering excellence.
Ability to communicate complex architectural decisions clearly to both technical teams and executive leadership.
Experience leading or mentoring engineering teams through rapid scaling phases.
Fluency in Python and modern cloud infrastructure (AWS, PostgreSQL, event-driven architectures).
Strong systems thinking and the ability to balance speed with maintainability.

Nice to Have

Experience building AI/ML platforms or systems that serve real-time insights to business users.
Background in revenue technology, CRM platforms, or enterprise GTM systems.
Track record of building systems that maintain trust and accuracy as they scale — not just systems that handle load.
Experience implementing data governance, validation, and monitoring frameworks across business-critical systems.
Familiarity with AI evaluation frameworks and quality assurance for LLM-powered outputs.