Staff Machine Learning Engineer, Consumer Risk AI

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 workstreams, 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

• Minimum
• BS, MS, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience
• 8+ 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 in Python and SQL; production experience with Spark, Flink, or equivalent for streaming and batch data processing
• Demonstrated ownership of a data or ML platform used by more than one team, including the operational load after launch
• Experience deploying models to real-time serving under hard latency budgets, and running them in production afterward
• Cloud infrastructure depth in at least one major cloud, ideally AWS (including SageMaker or equivalent ML tooling). Comfortable owning your own footprint — IaC, CI/CD, cost — rather than filing tickets against someone else's
• Track record of setting technical direction in ambiguous problem spaces and bringing other teams along without formal authority
• Strong written communication. You can put a tradeoff in front of an AI scientist, a platform owner, and a risk strategy partner in one document and have all three follow it
• Preferred
• Risk, fraud, payments, or credit domain experience, ideally in real-time decisioning
• Feature store or feature platform experience
• Entity resolution or identity graph work
• Experience with a rules engine and the model-to-decision handoff
• Regulated data handling — field-level encryption, fine-grained access control, data governance in financial services
• Fluency orchestrating AI agents on real engineering work, and judgment about where agent-generated output needs deterministic guardrails

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.