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About This Role
Intuit’s Consumer Group, including TurboTax and Credit Karma, empowers millions of individuals to take control of their finances. TurboTax simplifies tax preparation and enables our customers to file with confidence. By harnessing the power of data and artificial intelligence (AI), we continuously innovate and evolve our consumer offerings to deliver even greater value.
As we expand into Consumer Lending within the Consumer Group, Intuit Credit Karma is looking for an innovative, experienced, and hands\-on Staff AI Scientist to join our Consumer Risk AI Science team. In this role, you’ll develop cutting\-edge credit risk AI/ML models for new lending products. Join a collaborative and inventive team of AI scientists and machine learning engineers where your work will have a direct impact on hundreds of thousands of customers.
Responsibilities
### What you’ll do:
- Contribute to the credit risk AI science initiatives for the new and evolving Money product offerings focusing on the lending domain, including complete hands\-on ownership of the model lifecycle, sharing ownership of success and key results at the program\-level, and driving the data strategy across all involved teams.
+ Design, build, deploy, evaluate, defend, and monitor machine learning models to predict credit risk for various short\-term lending products (e.g., tax refund advances, BNPL, installment loans, line of credit, and early wage access)
+ Collaborate with credit policy, product and fraud risk teams to ensure models align with business goals and product offering to drive actionable lending decisions
+ Build efficient and reusable data pipelines for feature generation, model development, scoring, and reporting using Python, SQL, and both commercially available and proprietary Machine Learning and AI infrastructures
+ Deploy models in a production environment in collaboration with other AI scientists and machine learning enginers
+ Ensure model fairness, interpretability, and compliance with FCRA, ECOA, and other relevant regulatory frameworks
- Build next\-generation credit risk models for short\-term lending products using advanced deep learning techniques (e.g., transformers, sequence models, and representation/embedding learning on tabular and time\-series financial data)
- Build and improve transaction categorization models that power cash flow underwriting and credit risk models for thin\-file and sub\-prime consumers.
- Contribute to the evolution of our data and machine learning infrastructure within the Intuit ecosystem to improve efficiency and effectiveness of AI science solutions.
- Research and implement practical and creative machine learning and statistical approaches suitable for our fast\-paced, growing environment.
- Design, build, and deploy AI agents and orchestration workflows powered by Agentic AI to automate the end\-to\-end model development lifecycle—data exploration, feature engineering, data validation, model training, evaluation, and monitoring—accelerating team velocity and productivity.
Qualifications
### Minimum Basic Requirements:
- Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline
- 8\+ years of work experience in AI Science / Machine Learning and related areas
- Authoritative knowledge of Python and SQL
- Relevant work experience in fintech credit risk, with deep understanding of payment systems, money movement products, banking, and lending
- Experience leveraging credit bureau, tax and cash flow data in credit risk model development
- Deep, hands\-on expertise developing, deploying, monitoring and maintaining a variety of machine learning techniques, including but not limited to, deep learning (transformers, sequence modeling), tree\-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing.
- Deep understanding of credit risk modeling concepts, including PD calibration, reject inference, adverse action logic, and risk segmentation
- Ability to quickly develop a deep statistical understanding of large, complex datasets
- Expertise in designing and building efficient and reusable data pipelines and framework for machine learning models
- Strong business problem solving, communication and collaboration skills
- Ambitious, results oriented, hardworking, team player, innovator and creative thinker
- Proven experience defining and driving end\-to\-end modeling frameworks, methodologies, or best practices across multiple product teams or domains.
- Demonstrated ability to evaluate and integrate emerging AI/ML technologies, contributing to the company’s external technical visibility and innovation agenda.
### Preferred Qualifications:
- Proficiency in deep learning ML frameworks such as TensorFlow, PyTorch, etc.
- Work experience with public cloud platforms (especially GCP or AWS) and workflow orchestration tools like Apache Airflow
- Strong background in MLOps infrastructure and tooling, particularly Vertex AI or AWS SageMaker, including pipelines, automated retraining, monitoring, and version control
- Experience with experimentation design and analysis, including A/B testing and statistical analysis.
- Working knowledge of LLMs and AI agents (prompt engineering, RAG, tool calling, agentic workflows) and familiarity with orchestration frameworks (e.g., LangChain, LangGraph) and the Gen AI stack (embeddings, vector databases, fine\-tuning).
- Experience building transaction categorization and cash flow modeling pipelines from bank/aggregator data (e.g., Plaid, Nova Credit, MX, Finicity) for credit risk or underwriting use cases.
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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 $226,000 \- $306,000
Salary Context
This $226K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Intuit, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($266K) sits 24% above the category median. Disclosed range: $226K to $306K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Intuit AI Hiring
Intuit has 12 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span New York, NY, US, Mountain View, CA, US, San Diego, CA, US. Compensation range: $190K - $328K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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