Machine Learning Engineer, Consumer Risk AI

Position: Staff Machine Learning Engineer, Consumer Risk AI
Company Overview Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With tens of millions of customers worldwide using products such as Turbo Tax, Credit Karma, Quick Books, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview Intuit is looking for a Staff Machine Learning Engineer to own the data and platform layer beneath our consumer risk decisioning. This team builds the machine learning that decides, in real time, whether money moves — protecting customers from account takeover, first/third-party fraud, and unauthorized transactions across Intuit Fintech products.

You'll design and own the shared infrastructure every model on this team depends on: streaming and batch feature pipelines, the cross-entity data path, training and evaluation frameworks, real-time inference serving, and the handoff into our decision engine, across entire Fintech money product lifecycle — from risk screening/qualification of money-in/out events, cashflow underwriting,account take-over detection, to dynamic segmentation. What you architect becomes the reference pattern the whole organization adopts.

Responsibilities Own the technical vision and architecture for the consumer risk data and serving platform — feature pipelines, the cross-entity data path, training and evaluation infrastructure, and real-time inference — balancing tradeoffs and long-term implications Design and build multi-cloud infrastructure stack and enable data handshake, working through federated account link mapping, and land curated, governed datasets in the Intuit's central data lake Build shared feature infrastructure spanning streaming and batch, consumed by multiple model work streams, with the observability to catch drift and staleness

Establish evaluation frameworks that make model quality, regression, and production impact measurable across the team's portfolio

Own the model-to-decision path: deployment through model serving, integration with the decision engine, and correctness and latency on a sub-second decision budget

Set and enforce engineering standards for ML systems on this team — testing, observability, reproducibility, operational excellence — and structure codebases for agent-assisted development and autonomous navigation

Engineer closed-loop workflows that automate the repetitive parts of the model lifecycle, moving beyond point automation toward orchestrated systems needing minimal intervention

Eliminate barriers caused by technical and prioritization complexity, including dependencies that cross into platform and data teams — cultivate the partnerships that make those dependencies tractable

Generalize what you build into a reference pattern other teams can adopt rather than rebuild, and document it so the pattern travels

Mentor engineers on ML systems craft; provide actionable feedback to senior engineers and help them break work into pieces agents can execute reliably

Connect technical decisions to the metrics leadership tracks — loss basis points, approval rate, decision latency, hold release rate — define success up front, and drive the post-launch iteration

Qualifications

MinimumBS, MS, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience8+ years building production software, with substantial time on ML systems rather than ML research; prior experience leading an engineering effort across teams

Strong CS fundamentals — data structures, algorithms, distributed systems, system design — plus working ML fundamentals (classification, regression, feature engineering, model evaluation)
Proficiency…