Staff Engineer, GTM AI
procore · US - Texas - Austin ·
- Seniority
- Staff
- Experience
- 8+ years
- Visa sponsorship
- Yes
- Company size
- 1000+
- Salary
- USD 168,560 – 231,770 / year
procore · US - Texas - Austin ·
We are seeking a Staff Engineer, GTM AI 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
At Procore, AI isn’t a specialized tool, it's a core competency. We expect every team member to be AI-literate, leveraging generative tools and agentic workflows to move faster and work smarter. You won’t just use AI; you’ll be building the agentic future of construction.
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.
Requirements
6–8+ years of software engineering experience, with a proven track record of owning complex system architectures.
Deep expertise building secure, scalable enterprise platforms, with proven experience designing strictly idempotent APIs for high-throughput distributed systems.
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.
Base Pay Range:
168,560.00 - 231,770.00 USD AnnualThis role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.
Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.