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
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- 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.
The expected base pay range for this position is:
Mountain View $202,500 - $274,000