Staff ML Engineer: Scale Models & Data Pipelines

Intuit; Southfield (MI); On-site; USD 203,000 - 274,000; Full time

Benefits offered by this job

Cash bonus

Equity rewards

Benefits package

Job summary

Intuit is hiring a Staff Machine Learning Engineer to be embedded with a data science team and contribute to end-to-end model development at scale. You will wrangle data, design features, and deploy robust models using modern ML tools.

The role emphasizes collaboration with product managers and engineers and requires strong communication of complex concepts. You will leverage cloud platforms (AWS/GCP), GPUs for accelerated training, and a focus on performance and reliability.

Qualifications

• BS, MS, or PhD in Computer Science or related field, or equivalent work experience.
• 6+ years of experience in machine learning engineering.
• Proficiency with data science tools and frameworks (Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
• Basic knowledge of machine learning techniques (classification, regression, clustering).
• Understand ML training/validation principles and data query/processing (SQL).
• Strong software engineering fundamentals: Git/GitHub, production-ready code, scalable systems.

Responsibilities

• Discover data sources, access and clean data for ML readiness.
• Collaborate with data scientists to build features and pipelines for model training and deployment.
• Design and implement ML algorithms with product impact in mind.
• Run A/B tests, analyze results, and iterate on models based on data insights.
• Communicate results to peers and leaders across teams.
• Explore new tech trends to connect to customer benefits.

Skills

6+ years experience

Education

BS/MS/PhD in CS

Tools

Python

TensorFlow

Keras

Pandas

NumPy

SQL

Spark

AWS

GCP

Git

Job description

Intuit is hiring a Staff Machine Learning Engineer to be embedded with a data science team and contribute to end-to-end model development at scale. You will wrangle data, design features, and deploy robust models using modern ML tools.

The role emphasizes collaboration with product managers and engineers and requires strong communication of complex concepts. You will leverage cloud platforms (AWS/GCP), GPUs for accelerated training, and a focus on performance and reliability.