Intuit, the worldwide fintech platform behind TurboTax, Credit Karma, QuickBooks and Mailchimp, is expanding its risk-decisioning capabilities. The company’s mission is to give individuals and businesses the tools they need to prosper, and safeguarding money movement lies at the heart of that promise.
Based in Mountain View, California, the next Staff Machine Learning Engineer will own the data and platform layer that fuels consumer-risk decisions across Intuit’s suite of financial products. This role is central to protecting customers from account takeover, fraud, and unauthorized transactions in real time.
About the role and the business unit
The position sits within the Consumer Risk team, which builds the machine-learning models that evaluate every inbound or outbound money event. The engineer will design the shared infrastructure that supports all models – from streaming and batch feature pipelines to real-time inference serving and hand-off into the decision engine. The work spans the entire fintech money-product lifecycle, covering risk screening, cash-flow underwriting, takeover detection and dynamic segmentation.
Why the role matters
Every millisecond counts when a transaction is evaluated. The architecture you create becomes the reference pattern adopted across Intuit, influencing how the organization balances speed, accuracy and regulatory compliance.
Key responsibilities
As the technical leader, you will:
- Define and execute the vision for a cross-entity data path, training and evaluation frameworks, and sub-second inference serving.
- Build a multi-cloud infrastructure stack handling federated account-link mapping and curating governed datasets in Intuit’s central data lake.
- Develop shared feature infrastructure that works for both streaming and batch workloads, equipped with observability tools to detect drift and staleness.
- Establish rigorous evaluation frameworks that surface model regression and production impact across the portfolio.
- Own the end-to-end model-to-decision pipeline, ensuring correct, sub-second responses when the decision engine processes a request.
- Set and enforce engineering standards for testing, reproducibility, observability and operational excellence.
- Engineer closed-loop workflows that automate repetitive lifecycle steps, moving from point automation to orchestrated systems.
- Break down technical and prioritization barriers by partnering with platform and data teams, turning dependencies into tractable collaborations.
- Document and disseminate the reference pattern so other groups can adopt it without reinventing the wheel.
- Mentor engineers on ML-system craftsmanship, providing actionable feedback and guiding autonomous development.
- Connect technical decisions to business metrics such as loss basis points, approval rates, decision latency, and hold-release rates.
Required qualifications and preferred experience
To succeed, candidates need:
- A bachelor’s, master’s or PhD in Computer Science, Engineering, or a comparable quantitative field, or equivalent practical experience.
- At least eight years of production software engineering, with a substantial focus on machine-learning systems rather than pure research.
- Strong fundamentals in data structures, algorithms, distributed systems and system design, plus hands-on knowledge of classification, regression, feature engineering and model evaluation.
- Proficiency in Python and SQL and production experience with Spark, Flink or equivalent streaming/batch engines.
- Demonstrated ownership of a data or ML platform serving multiple teams, including post-launch operational responsibilities.
- Experience deploying models to real-time serving under hard latency budgets and maintaining them in production.
- Deep familiarity with at least one major cloud (ideally AWS including SageMaker or comparable ML tooling), and comfort managing infrastructure-as-code, CI/CD pipelines and cost controls.
- Proven ability to set technical direction in ambiguous spaces and influence cross-functional partners without formal authority.
- Excellent written communication skills to articulate trade-offs to AI scientists, platform owners and risk strategists.
Preferred assets include background in risk, fraud, payments or credit domains, experience with feature stores, entity resolution, rules engines, regulated data handling (field-level encryption, fine-grained access control), and orchestrating AI agents with deterministic guardrails.
Compensation, benefits and application details
Intuit offers a competitive pay-for-performance package that may include a cash bonus, equity awards and comprehensive benefits. Salary for this role in Mountain View ranges from $202,500 to $274,000 base, adjusted for knowledge, skills, experience and location. The company also conducts regular equity-pay analyses to ensure fairness across ethnicity and gender groups.
Applicants interested in shaping the future of fintech risk protection can submit their materials using Job ID 23962. The position promises high impact, exposure to cutting-edge ML infrastructure and the chance to influence a global financial platform used by millions.
