Staff ML Engineer - Scalable Models & Pipelines

Intuit; West Lafayette (IN); On-site; USD 203,000 - 274,000; Full time

Benefits offered by this job

Cash bonus

Equity rewards

Benefits

Job summary

Intuit is seeking a Staff Machine Learning Engineer to embed within a dynamic data science team. You will conceive, code, and deploy scalable ML models, focusing on data wrangling, feature engineering, and evaluation to drive customer impact.

You will partner with data scientists, product managers and engineers, run regular A/B tests, and explore new technologies to deliver measurable benefits at scale.

Qualifications

• BS, MS or PhD in Computer Science or related field, or equivalent work experience.
• 6+ years of experience.
• Proficient with Python and ML frameworks (TensorFlow, Keras, Scikit-learn, Spark).
• Knowledge of ML concepts (training, validation, evaluation).
• SQL and data processing skills for feature engineering and analytics.
• Experience deploying scalable ML systems in production.
• Familiar with cloud platforms (AWS, GCP) and GPU acceleration.

Responsibilities

• Discover data sources, access and clean them to be ML-ready.
• Collaborate with data scientists to create and refine features and pipelines.
• Design, train, and deploy ML models and benchmarks.
• Run A/B tests, analyze results, and communicate impact to stakeholders.
• Collaborate with product managers and engineers and present findings clearly.
• Explore new tech shifts to enhance customer value and impact.

Skills

Machine learning

A/B testing

Data wrangling

Feature engineering

Model deployment

Python

SQL

Cloud computing

Education

BS/MS/PhD in Computer Science or related field

Tools

TensorFlow

Keras

Scikit-learn

Apache Spark

CUDA

cuDNN

AWS

GCP

Numpy

Pandas

Job description

Intuit is seeking a Staff Machine Learning Engineer to embed within a dynamic data science team. You will conceive, code, and deploy scalable ML models, focusing on data wrangling, feature engineering, and evaluation to drive customer impact.

You will partner with data scientists, product managers and engineers, run regular A/B tests, and explore new technologies to deliver measurable benefits at scale.