Overview
The team
Money Movement Risk AI/ML is a team of AI scientists and ML engineers in Intuit's Fintech Risk organization. Our models decide, in real time, whether money moves for small businesses on QuickBooks: card and ACH payments, money coming in, money going out, when funds are released, and how long a payment is held.
Every decision has two failure modes. Approve the wrong transaction and Intuit takes a fraud or credit loss. Decline or hold the right one and a small business waits for money it has already earned. Our job is to push both down at the same time.
Our mandate: AI touches every money movement risk decision. That covers deciding (authorize, fund, release), acting on the decision (queue, investigate, hold), and accounting for it (expected loss).
The role
You will be the technical owner of one money movement risk capability: its models, its evaluation, its production health, and its business outcome. You will design, train, validate, and ship the models yourself, and you will set direction for the scientists and engineers who build with you.
This role is for an AI producer. You will build new models from first principles and put them in front of live money. It is not a role for wiring an existing LLM into a product surface.
What you will work on
Depending on fit and team needs, you will own one or more of these areas:
Authorization risk. Real-time fraud and loss models that approve, decline, or step up card and ACH transactions, for both money coming in and money going out.
Funding and settlement risk. Models that decide when funds are released and how long payments are held or delayed, so good customers get paid faster and risky payments are caught before money leaves.
Balance and account-level money risk. Risk on stored balances and on the accounts behind them, as more money stays inside Intuit.
Day-1 risk for new money products. Risk decisioning that is ready when a new payment or funding product launches, often with almost no labeled history.
Agentic money movement. Transaction risk for payments and money actions initiated by AI agents and assistants: agent identity, consent, and intent.
Risk-ranked queueing. Ranking cases for risk operations by dollars at risk, so analysts spend time where the loss is.
Responsibilities
What you will do
Own outcomes, not just models. Define success in loss, approval rate, hold rate, and time-to-funds. Ship models that move those numbers, and own them in production.
Solve the hard statistics of risk. Delayed and noisy labels (chargebacks and returns arrive weeks later), extreme class imbalance, and selection bias from models whose decisions shape their own training data. Use holdouts, reject inference, and counterfactual evaluation to measure what actually happened.
Build for adaptive adversaries. Detect fraud rings, account takeover, and new attack patterns with graph, sequence, and anomaly-detection methods. Test models against attackers who probe, retry, and adapt.
Make models production-grade. Partner with ML engineers on real-time features, low-latency serving, automated retraining, and monitoring for drift, data quality, and loss.
Turn Intuit's data into an advantage. Build reusable, versioned features and labels from transaction, account, and network data that other models on the team can build on.
Set the evaluation bar. Establish offline and online evaluation, experimentation, and adversarial testing standards for your area.
Lead across functions. Work with risk policy, product, engineering, risk operations, and finance. Explain trade-offs between loss and friction in plain terms and drive decisions.
Raise the team. Mentor senior and early-career scientists and engineers, lead design and model reviews, and help hire.
Qualifications
What you bring
Required
8+ years building and deploying ML models in production, including significant work in fraud, payments, credit, or another adversarial or financial-risk domain.
MS or PhD in Statistics, Computer Science, Applied Mathematics, Operations Research, or a related field, or equivalent practical experience.
A track record of owning models that made real-time, high-stakes decisions, with measured business impact.
Deep expertise in gradient-boosted trees, deep learning, and anomaly detection, plus the statistics to validate them: calibration, imbalance, delayed labels, and selection bias.
Strong Python and SQL, and hands-on large-scale feature engineering with Spark or similar.
Experience shipping models with ML engineers: real-time features, serving, retraining, monitoring.
Sound experimental judgment in a risk setting: A/B tests, holdouts, and their limits when attackers can exploit them.
Clear communication with risk, product, and business leaders.
Preferred
Payments risk experience: card and ACH authorization, funding, chargebacks, returns, or settlement.
Graph ML or network analysis for fraud rings and collusion.
Sequence or representation learning on transaction and behavioral data.
Experience building or evaluating LLM and agentic systems, especially for risk or operations.
Model risk management and explainability in a regulated financial-services environment.
Experience leading a technical area across teams and mentoring scientists.
Why join
Your work moves real money. Your models decide whether small businesses get paid, and how fast.
Hard problems. Millisecond decisions, adaptive attackers, labels that arrive weeks late, and models that shape their own training data.
Full ownership. Scientists here own their models from problem framing to production. Nobody hands you a spec.
An AI-native risk org. We are building toward AI making every money movement risk decision, and this role helps define how.
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 (https://www.intuit.com/careers/benefits/full-time-employees/) ). 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 $209,500 - $283,500
EOE AA M/F/Vet/Disability. Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.
