RESUME AND JOB
Intuit
At Intuit, a Staff ML Engineer acts as a bridge between research (Data Science) and engineering (Production). You are not just building models; you are architecting the systems that allow those models to serve 100 million+ customers across products like QuickBooks
Your work will likely align with one of Intuit’s 'Big Bets' in AI:
Hyper-Personalization: Building recommendation engines that analyze financial history to offer tailored advice (e.g., specific tax deductions or cash flow forecasts).
AI-Driven Expert Platform: Automating complex financial workflows to connect customers with human experts only when necessary.
Financial Fraud Detection: Developing deep learning models to detect anomalies in transaction data in real-time.
Intuit uses a modern, cloud-native stack. You should be proficient in:
| Category | Technologies |
| Languages | Python (primary), Java, Scala, SQL |
| ML Frameworks | PyTorch, TensorFlow, Scikit-learn, Keras |
| Big Data & Processing | Apache Spark, Kafka, Databricks |
| Cloud & Infrastructure | AWS (SageMaker), Kubernetes (K8s), Docker, Kubeflow |
| GenAI / LLMs | LangChain, Bedrock, Proprietary LLMs, GenOS |
Responsibilities
Architecting ML Platforms: Design scalable, fault-tolerant systems that can handle massive throughput for real-time predictions (e.g., fraud detection during tax filing).
GenAI Integration (GenOS): Leverage Intuit’s proprietary GenOS (Generative AI Operating System) to integrate Large Language Models (LLMs) into products, powering features like 'Intuit Assist.'
Model Productionalization: Take experimental models from Data Scientists (often written in notebooks) and refactor/optimize them for production (latency, reliability, scalability).
Cross-Functional Leadership: Serve as the technical lead for major initiatives, coordinating between Data Scientists, Product Managers, and Backend Engineers.
Technical Standards: Define best practices for code quality, testing, and ML Ops (CI/CD for ML) across the organization.
Qualifications
Bachelors of Engineering or Above equivalent from prestigiuos Instititutions
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 (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:
800,000 - 1,500,000 INR / yearly
Source: AI Estimation
* This is an estimated range based on market data and may vary based on experience and qualifications.
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Intuit
At Intuit, a Staff ML Engineer acts as a bridge between research (Data Science) and engineering (Production). You are not just building models; you are architecting the systems that allow those models to serve 100 million+ customers across products like QuickBooks
Your work will likely align with one of Intuit’s 'Big Bets' in AI:
Hyper-Personalization: Building recommendation engines that analyze financial history to offer tailored advice (e.g., specific tax deductions or cash flow forecasts).
AI-Driven Expert Platform: Automating complex financial workflows to connect customers with human experts only when necessary.
Financial Fraud Detection: Developing deep learning models to detect anomalies in transaction data in real-time.
Intuit uses a modern, cloud-native stack. You should be proficient in:
| Category | Technologies |
| Languages | Python (primary), Java, Scala, SQL |
| ML Frameworks | PyTorch, TensorFlow, Scikit-learn, Keras |
| Big Data & Processing | Apache Spark, Kafka, Databricks |
| Cloud & Infrastructure | AWS (SageMaker), Kubernetes (K8s), Docker, Kubeflow |
| GenAI / LLMs | LangChain, Bedrock, Proprietary LLMs, GenOS |
Responsibilities
Architecting ML Platforms: Design scalable, fault-tolerant systems that can handle massive throughput for real-time predictions (e.g., fraud detection during tax filing).
GenAI Integration (GenOS): Leverage Intuit’s proprietary GenOS (Generative AI Operating System) to integrate Large Language Models (LLMs) into products, powering features like 'Intuit Assist.'
Model Productionalization: Take experimental models from Data Scientists (often written in notebooks) and refactor/optimize them for production (latency, reliability, scalability).
Cross-Functional Leadership: Serve as the technical lead for major initiatives, coordinating between Data Scientists, Product Managers, and Backend Engineers.
Technical Standards: Define best practices for code quality, testing, and ML Ops (CI/CD for ML) across the organization.
Qualifications
Bachelors of Engineering or Above equivalent from prestigiuos Instititutions
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 (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:
800,000 - 1,500,000 INR / yearly
Source: AI Estimation
* This is an estimated range based on market data and may vary based on experience and qualifications.
Get personalized recommendations to optimize your resume specifically for Staff Machine Learning Engineer. Takes only 15 seconds!
Find out how well your resume matches this job's requirements. Get comprehensive analysis including ATS compatibility, keyword matching, skill gaps, and personalized recommendations.
Answer 10 quick questions to check your fit for Staff Machine Learning Engineer @ Intuit.

No related jobs found at the moment.

© 2026 Pointers. All rights reserved.