Base Salary
$203k - $274k/yr
Responsibilities
- Own the technical vision and architecture for the consumer risk data and serving platform, including feature pipelines, cross-entity data, training and evaluation infrastructure, and real-time inference.
- Design and build multi-cloud infrastructure and data handshakes, including federated account-link mapping and governed datasets in Intuit’s central data lake.
- Build shared streaming and batch feature infrastructure with observability for drift and staleness.
- Establish evaluation frameworks for model quality, regression, and production impact.
- Own deployment, real-time model serving, decision-engine integration, correctness, and sub-second latency.
- Set engineering standards for testing, observability, reproducibility, and operational excellence in ML systems.
- Automate repetitive model lifecycle work through orchestrated, low-intervention workflows.
- Create reusable reference patterns, document them, and drive adoption across teams.
- Mentor engineers on ML systems practices and provide actionable feedback to senior engineers.
- Connect technical decisions to business metrics such as loss basis points, approval rate, decision latency, and hold release rate, and drive post-launch iteration.
Requirements
- Bachelor’s, master’s, or doctoral degree in computer science, engineering, or a related quantitative field, or equivalent practical experience.
- 8+ years of production software development experience, including substantial work on ML systems rather than ML research, and prior cross-team engineering leadership.
- Strong foundations in data structures, algorithms, distributed systems, system design, classification, regression, feature engineering, and model evaluation.
- Proficiency in Python and SQL, with production experience using Spark, Flink, or equivalent streaming and batch data-processing technologies.
- Demonstrated ownership of a data or ML platform used by multiple teams, including its post-launch operational responsibilities.
- Experience deploying and operating models in real-time serving environments with hard latency requirements.
- Cloud infrastructure depth in at least one major cloud, ideally AWS with SageMaker or equivalent ML tooling, and comfort owning infrastructure, infrastructure-as-code, delivery pipelines, and cost.
- Experience setting technical direction in ambiguous environments and influencing teams without formal authority.
- Strong written communication across AI science, platform, and risk strategy stakeholders.
- Preferred experience includes risk, fraud, payments, credit, real-time decisioning, feature stores or platforms, entity resolution, identity graphs, rules engines, regulated financial-services data, and AI-agent orchestration with deterministic guardrails.
Benefits
- Competitive compensation package with performance-based rewards.
- Position may be eligible for a cash bonus, equity rewards, and benefits under applicable plans and programs.
- Expected base pay range is $202,500–$274,000 for Mountain View, with pay determined by factors including skills, experience, and work location.
Tech Stack
Categories
About Intuit
Intuit is a global technology platform that helps our customers and communities overcome their most important financial challenges. Serving millions of customers worldwide with TurboTax, QuickBooks, Credit Karma and Mailchimp, we believe that everyone should have the opportunity to prosper and we work tirelessly to find new, innovative ways to deliver on this belief. We encourage conversations on this page and will not delete comments that follow our terms of use. In order to keep this a safe community, the below posts may be removed: Repeated posts of the same content, spam or posts from fake accounts or profiles, offensive language or material, threats to others in the community, posts deliberately aimed to have a negative effect on the community or conversations.