refresh

트렌딩 기업

트렌딩

채용

JobsIntuit

Staff Machine Learning Engineer Bangalore, India

Intuit

Staff Machine Learning Engineer Bangalore, India

Intuit

bengaluru

·

On-site

·

Full-time

·

2w ago

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:

LanguagesPython (primary), Java, Scala, SQLML FrameworksPyTorch, TensorFlow, Scikit-learn, KerasBig Data & ProcessingApache Spark, Kafka, DatabricksCloud & InfrastructureAWS (SageMaker), Kubernetes (K8s), Docker, KubeflowGenAI / LLMsLangChain, 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: 



Total Views

0

Apply Clicks

0

Mock Applicants

0

Scraps

0

About Intuit

Intuit

Intuit provides financial software solutions to empower individuals and businesses.

10,001+

Employees

Mountain View

Headquarters

$140B

Valuation

Reviews

3.6

9 reviews

Work Life Balance

3.8

Compensation

3.2

Culture

3.1

Career

3.7

Management

3.0

65%

Recommend to a Friend

Pros

Flexible schedule and work independence

Good benefits and 401k match

Supportive teammates and collaboration

Cons

Management issues and favoritism

High pressure and quotas

Poor communication and politics

Salary Ranges

91 data points

Junior/L3

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Data Scientist

5 reports

$150,492

total / year

Base

$115,763

Stock

-

Bonus

-

$138,970

$163,540

Interview Experience

7 interviews

Difficulty

3.0

/ 5

Duration

14-28 weeks

Offer Rate

14%

Experience

Positive 14%

Neutral 86%

Negative 0%

Interview Process

1

Application Review

2

Online Assessment/Technical Screen

3

Live Coding Interview

4

Case Study/Technical Assessment

5

Behavioral Interview

6

Offer

Common Questions

Coding/Algorithm

Technical Knowledge

Behavioral/STAR

Case Study

System Design