AI Engineer Jobs at Intuit with Visa Sponsorship
Intuit hires AI Engineers to build machine learning infrastructure, intelligent financial products, and LLM-powered features across TurboTax, QuickBooks, and Credit Karma. The company has a consistent track record of sponsoring work visas for technical roles, making it a realistic target if you need sponsorship.
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Overview
Come join Intuit as a Senior Staff Machine Learning Engineer (MLE). Senior Staff MLEs deliver end-to-end AI solutions that span multiple domains and products, influencing the strategic direction of machine learning and AI across the company. You will identify cross-cutting opportunities, set technical direction for complex systems, and deliver scalable, responsible AI-driven experiences that unlock customer and business value at Intuit scale. In this role, you’ll be expected to define and evolve ML architecture, guide multiple teams, and drive execution excellence across the full ML lifecycle—from experimentation to production. You’ll partner closely with AI scientists, product engineers, and business leaders to solve high-impact problems and pioneer new capabilities that advance Intuit’s AI-native platform.
Responsibilities
Technical Craft:
- Lead the architectural design of complex, cross-cutting ML systems and data platforms that serve multiple Intuit products.
- Drive the adoption of AI-native design principles, ensuring that systems are built for adaptability, observability, and secure customer data usage.
- Build and scale end-to-end ML solutions using cloud-native and open-source technologies (e.g., AWS, GCP, TensorFlow, PyTorch, Ray, Spark).
- Define engineering standards, model governance, and MLOps best practices across teams for training, deployment, monitoring, and continuous improvement.
- Evaluate and integrate transformative technologies such as foundation models, retrieval-augmented generation (RAG), and LLM fine-tuning pipelines to accelerate product innovation.
- Resolve deeply complex issues across domains, often requiring novel solutions or architectural evolution for long-term scalability.
Execution Excellence:
- Deliver within large-scale strategic initiatives, identifying systemic architectural gaps and leading their resolution across multiple teams.
- Challenge roadmaps to achieve measurable outcomes in weeks—not months, while balancing technical risk, business priorities, and product velocity.
- Establish clear execution boundaries and integration contracts across teams to accelerate delivery while maintaining quality.
- Proactively monitor model and system performance, ensuring continuous improvement of reliability, fairness, and customer impact.
- Champion experimentation at scale—defining hypotheses, success metrics, and iterative validation frameworks that balance speed with rigor.
Customer-Centric Outcomes:
- Translate emerging customer behaviors and business trends into bold ML-driven solutions that redefine customer experiences across Intuit’s ecosystem.
- Collaborate with Product and Design to frame and validate high-risk, high-impact hypotheses through MVPs and data-driven experimentation.
- Lead initiatives that use customer signals, behavioral data, and competitive insights to identify unmet needs and shape Intuit’s AI roadmap.
- Drive the development and deployment of models that directly improve measurable customer outcomes—conversion, engagement, trust, and satisfaction.
- Balance rapid delivery with long-term technical sustainability, ensuring quality and performance at scale.
Accelerating Teams & the Organization:
- Act as a force multiplier, raising the technical bar across multiple ML and engineering teams (typically influencing 10–35 engineers).
- Mentor and develop Staff and Senior MLEs, building a strong culture of learning, quality, and execution excellence.
- Identify and drive resolution for cross-team bottlenecks—architectural, tooling, or communication-related—that limit productivity or scalability.
- Build alignment across AI, product, and infrastructure teams to accelerate delivery of strategic initiatives.
- Provide architectural guidance, influence design reviews, and serve as a key connector between product, data, and platform teams.
- Champion diversity of thought and inclusive innovation within the ML and engineering community at Intuit.
Intuit Provides a Competitive Compensation Package With a Strong Pay For Performance Rewards Approach.
The Expected Base Pay Range For This Position Is Bay Area California: $214,000 - $289,500
This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
Qualifications
- BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
- 8+ years of experience in software or ML engineering, with at least 3+ years at Staff level or equivalent leadership scope.
- Proven track record of architecting and delivering production-scale ML systems that impact millions of users.
- Expert in ML lifecycle management, feature engineering, and large-scale model deployment.
- Deep hands-on experience with modern ML frameworks and distributed systems (TensorFlow, PyTorch, Spark, Ray, Kubernetes, MLflow, etc.).
- Experience leading cross-functional initiatives spanning multiple product or platform.
- Strong background in software engineering fundamentals: algorithms, distributed systems, data pipelines, and performance optimization.
- Excellent communication and influence skills, capable of aligning technical direction with organizational strategy.
- Familiarity with LLMs, GenAI, and applied responsible AI practices is a strong plus.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. 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: Bay Area California $214,000 - $289,500.

Overview
Come join Intuit as a Senior Staff Machine Learning Engineer (MLE). Senior Staff MLEs deliver end-to-end AI solutions that span multiple domains and products, influencing the strategic direction of machine learning and AI across the company. You will identify cross-cutting opportunities, set technical direction for complex systems, and deliver scalable, responsible AI-driven experiences that unlock customer and business value at Intuit scale. In this role, you’ll be expected to define and evolve ML architecture, guide multiple teams, and drive execution excellence across the full ML lifecycle—from experimentation to production. You’ll partner closely with AI scientists, product engineers, and business leaders to solve high-impact problems and pioneer new capabilities that advance Intuit’s AI-native platform.
Responsibilities
Technical Craft:
- Lead the architectural design of complex, cross-cutting ML systems and data platforms that serve multiple Intuit products.
- Drive the adoption of AI-native design principles, ensuring that systems are built for adaptability, observability, and secure customer data usage.
- Build and scale end-to-end ML solutions using cloud-native and open-source technologies (e.g., AWS, GCP, TensorFlow, PyTorch, Ray, Spark).
- Define engineering standards, model governance, and MLOps best practices across teams for training, deployment, monitoring, and continuous improvement.
- Evaluate and integrate transformative technologies such as foundation models, retrieval-augmented generation (RAG), and LLM fine-tuning pipelines to accelerate product innovation.
- Resolve deeply complex issues across domains, often requiring novel solutions or architectural evolution for long-term scalability.
Execution Excellence:
- Deliver within large-scale strategic initiatives, identifying systemic architectural gaps and leading their resolution across multiple teams.
- Challenge roadmaps to achieve measurable outcomes in weeks—not months, while balancing technical risk, business priorities, and product velocity.
- Establish clear execution boundaries and integration contracts across teams to accelerate delivery while maintaining quality.
- Proactively monitor model and system performance, ensuring continuous improvement of reliability, fairness, and customer impact.
- Champion experimentation at scale—defining hypotheses, success metrics, and iterative validation frameworks that balance speed with rigor.
Customer-Centric Outcomes:
- Translate emerging customer behaviors and business trends into bold ML-driven solutions that redefine customer experiences across Intuit’s ecosystem.
- Collaborate with Product and Design to frame and validate high-risk, high-impact hypotheses through MVPs and data-driven experimentation.
- Lead initiatives that use customer signals, behavioral data, and competitive insights to identify unmet needs and shape Intuit’s AI roadmap.
- Drive the development and deployment of models that directly improve measurable customer outcomes—conversion, engagement, trust, and satisfaction.
- Balance rapid delivery with long-term technical sustainability, ensuring quality and performance at scale.
Accelerating Teams & the Organization:
- Act as a force multiplier, raising the technical bar across multiple ML and engineering teams (typically influencing 10–35 engineers).
- Mentor and develop Staff and Senior MLEs, building a strong culture of learning, quality, and execution excellence.
- Identify and drive resolution for cross-team bottlenecks—architectural, tooling, or communication-related—that limit productivity or scalability.
- Build alignment across AI, product, and infrastructure teams to accelerate delivery of strategic initiatives.
- Provide architectural guidance, influence design reviews, and serve as a key connector between product, data, and platform teams.
- Champion diversity of thought and inclusive innovation within the ML and engineering community at Intuit.
Intuit Provides a Competitive Compensation Package With a Strong Pay For Performance Rewards Approach.
The Expected Base Pay Range For This Position Is Bay Area California: $214,000 - $289,500
This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
Qualifications
- BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
- 8+ years of experience in software or ML engineering, with at least 3+ years at Staff level or equivalent leadership scope.
- Proven track record of architecting and delivering production-scale ML systems that impact millions of users.
- Expert in ML lifecycle management, feature engineering, and large-scale model deployment.
- Deep hands-on experience with modern ML frameworks and distributed systems (TensorFlow, PyTorch, Spark, Ray, Kubernetes, MLflow, etc.).
- Experience leading cross-functional initiatives spanning multiple product or platform.
- Strong background in software engineering fundamentals: algorithms, distributed systems, data pipelines, and performance optimization.
- Excellent communication and influence skills, capable of aligning technical direction with organizational strategy.
- Familiarity with LLMs, GenAI, and applied responsible AI practices is a strong plus.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. 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: Bay Area California $214,000 - $289,500.
See all 37+ AI Engineer at Intuit jobs
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Get Access To All JobsTips for Finding AI Engineer Jobs at Intuit Jobs
Align your ML experience to Intuit's product stack
Intuit's AI teams work heavily on NLP, personalization, and document understanding within financial products. Frame your resume around those domains rather than generic deep learning experience. Reviewers are looking for signal that you understand production ML at scale.
Target roles that sit under established AI orgs
Intuit has dedicated AI and data platform organizations. Roles posted under those orgs are more likely to have defined sponsorship workflows than newer or experimental teams, which sometimes lack the HR infrastructure to move quickly on visa filings.
Get your credentials evaluated before applying
If your degree is from outside the United States, get a credential evaluation from a NACES-approved evaluator before your first interview. Intuit's immigration team will need this for the H-1B specialty occupation determination, and delays here can slow the entire petition timeline.
Understand how the H-1B cap affects your start date
If you're not already on a cap-exempt status like F-1 OPT or an existing H-1B, your petition enters the annual lottery. USCIS cap-subject petitions can only take effect October 1, so negotiate your offer timeline accordingly and ask HR which filing window applies to you.
Clarify LCA scope during the offer stage
The Labor Condition Application filed with DOL locks in your worksite and wage level. Before signing, confirm whether Intuit will list their Mountain View or New York offices, especially if your role is hybrid. A mismatched LCA worksite can require an amendment if your location changes.
Use Migrate Mate to find open AI Engineer roles at Intuit that explicitly support sponsorship
Not every Intuit job posting makes sponsorship eligibility obvious. Migrate Mate filters specifically for roles where sponsorship is confirmed, saving you from applying to positions where visa support was never on the table.
AI Engineer at Intuit jobs are hiring across the US. Find yours.
Find AI Engineer at Intuit JobsFrequently Asked Questions
Does Intuit sponsor H-1B visas for AI Engineers?
Yes, Intuit sponsors H-1B visas for AI Engineer roles. The company works with immigration counsel to file cap-subject and cap-exempt petitions depending on your current status. If you're transitioning from F-1 OPT, Intuit can file during the regular cap season. If you hold an existing H-1B from another employer, a transfer is possible without waiting for the lottery.
How do I apply for AI Engineer jobs at Intuit?
Start by identifying open AI Engineer roles through Intuit's careers portal or Migrate Mate, which filters for positions with confirmed visa sponsorship. Tailor your application to reflect Intuit's specific AI focus areas: financial NLP, recommendation systems, and ML platform engineering. After applying, expect a recruiter screen followed by a multi-stage technical loop covering coding, ML system design, and product sense.
Which visa types does Intuit commonly use for AI Engineers?
Intuit sponsors H-1B, E-3, TN, and F-1 OPT and CPT for AI Engineer roles, depending on your nationality and current status. Australian citizens are eligible for the E-3, which bypasses the H-1B lottery. Canadian and Mexican nationals in qualifying engineering roles may qualify for TN status. For permanent residency, Intuit also supports EB-2 and EB-3 Green Card sponsorship.
What qualifications does Intuit expect for AI Engineer roles?
Most AI Engineer roles at Intuit require a bachelor's or master's degree in computer science, machine learning, or a related technical field. Hands-on experience with large-scale ML systems, Python, and at least one major ML framework is expected. Roles closer to production engineering also weight distributed systems knowledge. Research-oriented positions often look for published work or experience with LLM fine-tuning and evaluation pipelines.
How do I time my application around the H-1B filing calendar?
USCIS opens H-1B registration in early March each year for the cap lottery. To be included, you need a signed offer by late February at the latest. If selected, your petition cannot take effect before October 1. F-1 OPT holders can bridge this gap using a timely filed extension. Coordinate your interview and offer timeline with Intuit's recruiters well in advance of the March window.
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