This role pays more than
50%
of similar roles. Most pay
$202,800–$260,012
— the shaded band above.
At the midpoint, this role pays about
$238k
versus about
$231k
for comparable roles.
Based on 240 similar postings.
Employer
About Intuit
Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology
Intuit currently has
199 open roles
on FindRole.
Listed pay typically runs
$202,500–$274,000
across 180 roles with salary data.
Staff Machine Learning Engineer, Consumer Risk AI joins the team responsible for building machine learning systems that determine real-time movement of money to protect customers from fraud and unauthorized transactions. You will own the technical vision and architecture for the consumer risk data and serving platform, including streaming and batch feature pipelines, cross-entity data paths, training frameworks, and real-time inference serving. You will build multi-cloud infrastructure, establish evaluation frameworks, and engineer closed-loop workflows to automate the model lifecycle while ensuring sub-second decision latency. The role requires proficiency in Python, SQL, Spark, Flink, and AWS tools like SageMaker. Key responsibilities include setting engineering standards for ML systems, mentoring engineers, and designing reference patterns for the organization. You will solve complex problems regarding risk screening, cashflow underwriting, account takeover detection, and dynamic segmentation within a regulated financial services environment.
What does a Machine Learning Engineer earn in California?
Median $231150 from 188 postings across 28 companies.
Design and own the technical architecture for the consumer risk data and serving platform.
Build streaming and batch feature pipelines with observability to detect drift and staleness.
Develop multi-cloud infrastructure to land curated, governed datasets in a central data lake.
Establish evaluation frameworks to measure model quality, regression, and production impact.
Manage the model-to-decision path ensuring sub-second latency for real-time inference.
Set engineering standards for ML systems including testing, observability, and reproducibility.
Automate repetitive parts of the model lifecycle through orchestrated, closed-loop workflows.
Create scalable reference patterns and documentation that other teams can adopt across the organization.
What we're looking for
BS, MS, or PhD in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.
8+ years of experience building production software with substantial time spent on ML systems and leading engineering efforts across teams.
Strong CS fundamentals including data structures, algorithms, distributed systems, system design, and core ML concepts like feature engineering and model evaluation.
Proficiency in Python and SQL with production experience using Spark or Flink for streaming and batch data processing.
Demonstrated ownership of a data or ML platform used by multiple teams, including post-launch operational management.
Experience deploying models to real-time serving under hard latency budgets and managing cloud infrastructure (ideally AWS).
Track record of setting technical direction in ambiguous spaces and communicating complex trade-offs clearly in writing.
Risk/fraud domain experience, feature store experience, or regulated data handling experience (preferred).