Machine Learning Jobs at Intuit with Visa Sponsorship
Intuit hires Machine Learning engineers and scientists across its financial technology products, including TurboTax, QuickBooks, and Credit Karma. The company has a consistent track record of sponsoring work visas for ML roles and supports multiple visa pathways, making it a realistic target for international candidates.
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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.
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Get Access To All JobsTips for Finding Machine Learning Jobs at Intuit Jobs
Align your ML portfolio to Intuit's products
Intuit's ML teams work on financial forecasting, natural language processing for tax guidance, and fraud detection. Frame your portfolio projects around these domains before applying. A generic computer vision project is less compelling than demonstrated experience with structured financial data.
Clarify your visa type before the recruiter screen
Intuit sponsors several visa categories, but each has different timelines and filing requirements. Tell the recruiter your current status, your authorization expiration date, and whether you're cap-subject before the first technical screen to avoid late-stage mismatches.
Time your application around the H-1B cap cycle
If you need H-1B sponsorship, USCIS opens registration in March for an October 1 start. Plan your Intuit application so an offer can realistically land in February or early March, giving the legal team enough runway to register you in that lottery cycle.
Use OPT STEM extension strategically at Intuit
F-1 graduates in ML qualify for the 24-month STEM OPT extension, which gives you up to three years of work authorization. Confirm with HR that Intuit is enrolled in E-Verify, a requirement for STEM OPT, before you rely on this timeline in your planning.
Prepare for a multi-round technical process
Intuit's ML interviews typically include a coding screen, an ML system design round, and a domain-specific case. Having your Green Card or visa timeline documented clearly lets you negotiate start dates confidently after clearing all rounds without scrambling for authorization details.
Find open ML roles at Intuit through Migrate Mate
Searching for visa-friendly ML openings manually is slow. Use Migrate Mate to filter Intuit's current Machine Learning roles by visa type, so you're only reviewing positions where your sponsorship category is already confirmed as supported.
Machine Learning at Intuit jobs are hiring across the US. Find yours.
Find Machine Learning at Intuit JobsFrequently Asked Questions
Does Intuit sponsor H-1B visas for Machine Learning roles?
Yes, Intuit sponsors H-1B visas for Machine Learning positions. The company files petitions through the standard USCIS cap process, with registration typically opening in March for an October 1 start date. If you're currently on F-1 OPT, coordinating your offer timeline around the H-1B lottery cycle is critical to avoid a gap in work authorization.
How do I apply for Machine Learning jobs at Intuit?
Start by identifying open ML roles that align with your specialization, whether that's NLP, recommendation systems, or financial forecasting. Apply through Intuit's careers portal and be upfront about your visa status in early recruiter conversations. You can browse current visa-sponsored openings filtered by role and visa type on Migrate Mate before submitting a formal application.
Which visa types does Intuit commonly use for Machine Learning hires?
Intuit sponsors a range of visa categories for ML roles, including H-1B, E-3 for Australian citizens, TN for Canadian and Mexican nationals, F-1 OPT and CPT for students, J-1 for exchange visitors, and employment-based Green Cards through the EB-2 and EB-3 pathways. The right category depends on your nationality, education level, and current immigration status.
What qualifications does Intuit expect for sponsored Machine Learning roles?
Most ML positions at Intuit require a bachelor's degree at minimum in computer science, statistics, or a related quantitative field, with a master's or PhD common for senior and research-oriented roles. Hands-on experience with large-scale data pipelines, model deployment, and frameworks such as TensorFlow or PyTorch is expected. Domain knowledge in fintech, NLP, or fraud detection strengthens your candidacy significantly.
How long does the visa sponsorship process take after receiving an Intuit offer?
Timeline depends on your visa category. H-1B petitions filed under standard processing take three to six months after USCIS receives the petition, while premium processing reduces that to 15 business days. E-3 and TN visas can often be obtained within a few weeks of receiving the offer letter and supporting documents. PERM-based Green Card sponsorship, if initiated, typically takes one to two years before a petition is filed.
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