Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,319 postings in the last year. Stated ranges, not salaries paid.
Mountain View, California, United States; New York, United States
Actively hiring, first seen by us todayIntuit 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 $243,000 - $328,500 New York $240,000- $325,000Read the full posting
Define and deliver the governed data layer with versioned catalog, freshness SLAs and drift monitoring
Make new-signal onboarding self-serve so analysts can wire data into policies without tickets
Build the decision record logging data and versions behind every decision for backtesting and retraining
Evaluate third-party identity, device and behavioral data vendors and decide buy, build or retire
Set ownership and service agreements with upstream platforms that compute and serve features
Own the data boundary: define and deliver the governed data layer every risk decision passes through, with a versioned catalog, contracted freshness, and drift monitoring
take new-signal onboarding from an engineering project to an analyst workflow
ensure every decision is logged with the data and versions that produced it
continuously evaluate third-party identity, device, and behavioral data partners; decide what to buy, what to build, and what to retire
establish ownership and service agreements with the internal platforms that compute and serve our features
8+ years in Product Management, including sustained ownership of complex, technical, multi-team platform initiatives with accountability for measurable business outcomes.
Experience with data vendors: evaluating, integrating, and operationalizing third-party data for decisioning, and managing the coverage-accuracy-cost trade-off.
Bachelor's degree required; Master's degree or MBA preferred.
- Exceptional communication: you translate data architecture trade-offs into clear narratives for executives and win alignment across organizations you don't control. - Bachelor's degree required; Master's degree or MBA preferred. 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.
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,319 postings in the last year. Stated ranges, not salaries paid.
Day 0
Early. Similar jobs usually come down between day 6 and day 71.
At Intuit, jobs usually come down by about day 22.
From 4,624 similar postings that came down, and 65 of Intuit’s. Coming down is not always a hire.
60 open jobs
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New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Every risk decision is only as good as the data behind it. When a feature silently degrades, decisions drift and losses surface weeks later. When onboarding a new signal takes weeks of engineering work, fraud rings outrun the defense. The data layer is where risk platforms are won or lost.
We're hiring a Principal Product Manager to own the data and signals foundation of our risk platform: the single governed layer through which every rule, model, and AI agent consumes data. You'll build the contracted catalog of features, scores, and vendor attributes (with owners, freshness guarantees, and drift monitoring at the boundary), the decision-time data record that powers backtesting and model training, and the third-party data strategy that keeps our signals best in class.
Your customers are internal: risk analysts who need to self-serve data for new policies, data scientists who need full data parity between models and rules, engineers who need one contract instead of hundreds of point-to-point fetches, and operations teams who need the full picture behind a decision. You'll be measured on how fast and how safely all of them can move.
Responsibilities
Own the data boundary: define and deliver the governed data layer every risk decision passes through, with a versioned catalog, contracted freshness, and drift monitoring that catches degraded inputs in minutes instead of days. You own the results and the plans to get there.
Make data self-serve: take new-signal onboarding from an engineering project to an analyst workflow, so the person with the fraud-domain knowledge can wire data into a policy without a ticket.
Build the decision record: ensure every decision is logged with the data and versions that produced it, creating the joined history that backtesting, model retraining, and case investigation all consume.
Own the vendor portfolio: continuously evaluate third-party identity, device, and behavioral data partners; decide what to buy, what to build, and what to retire, balancing coverage, accuracy, and cost.
Set the contracts upstream: establish ownership and service agreements with the internal platforms that compute and serve our features, so changes reach risk decisions announced, not silently.
Own the metrics: instrument time-to-detect degraded data, time-to-onboard a new signal, data reuse across policies, and analyst self-serve rates; report progress and trade-offs to leaders across risk and platform teams.
Align and deliver: build strong relationships across engineering, data science, risk policy, and partner platform organizations; ruthlessly prioritize; set delivery timelines.
Qualifications
8+ years in Product Management, including sustained ownership of complex, technical, multi-team platform initiatives with accountability for measurable business outcomes.
Risk experience is required: you've worked in fraud, credit, identity, or financial risk and understand how data quality drives loss and false-positive outcomes.
Proven track record as a data platform PM: you've owned a feature store, data catalog, data contracts, or governed data layer serving production decision systems.
Experience with data vendors: evaluating, integrating, and operationalizing third-party data for decisioning, and managing the coverage-accuracy-cost trade-off.
Deep partnership with data science and ML/AI teams: you understand features, training/serving parity, and what models need from a data layer.
Strong technical fluency: streaming and batch data infrastructure, APIs, SLOs, observability, and real-time decisioning.
Quantitative rigor: comfortable setting numeric targets for freshness, latency, coverage, and reuse, and proving impact against them.
Exceptional communication: you translate data architecture trade-offs into clear narratives for executives and win alignment across organizations you don't control.
Bachelor's degree required; Master's degree or MBA preferred.
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:Selected passages from the saved posting. Check the full description for conditions and exceptions.
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 $243,000 - $328,500 New York $240,000- $325,000
Mountain View, California, United States; New York, United States
Working pattern and location restrictions need checking in the full posting.
No clear work-authorization passage found. Eligibility is unconfirmed.