Staff Machine Learning Engineer

Intuit

Location

Bengaluru

Employment

Full Time

Experience

0-1 years

Salary

₹Not Available

About the role

Focus on topics such as hyper-personalization, AI-driven expert platforms, and financial fraud detection, leveraging advanced machine learning techniques and cloud-native technologies.

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.

Full description

Job Overview 1. The Core Mission 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 2. Strategic Focus Areas 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. 3. Tech Stack 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