Staff ML Engineer - Scale AI Pipelines & Production

Intuit; Springfield (MO); On-site; USD 203,000 - 274,000; Full time

Job summary

Intuit in Springfield, MO is seeking a Staff Machine Learning Engineer to collaborate with AI scientists to build and deploy scalable ML models. You will create data pipelines, train and productionize models, and evaluate ML solutions with modern tooling.

The role demands deep ML knowledge and cloud experience and strong communication across teams. Candidates should have advanced degrees and a track record delivering production systems, performance optimization, and end-to-end ML lifecycle in

Qualifications

• BS, MS, or PhD in Computer Science or related field, or equivalent work experience.
• 7+ years of experience in ML/DS roles.
• Strong knowledge of ML techniques (classification, regression, clustering).
• Experience deploying ML models to production and monitoring performance.
• Proficiency with data processing and query tools (SQL).
• Familiarity with cloud platforms (AWS, GCP).
• Strong communication skills and ability to present complex concepts.

Responsibilities

• Collaborate with AI scientists to create and refine features from data and build pipelines to train and deploy models.
• Build machine learning feature pipelines that are production-ready.
• Partner with AI scientists to implement and refine ML algorithms and approaches.
• Run A/B tests, analyze data, and draw conclusions on model impact.
• Monitor and maintain production models in scalable environments.
• Work cross-functionally with product managers, AI scientists, and engineers to communicate results.

Skills

Python

Scikit-learn

NLTK

NumPy

Pandas

TensorFlow

Keras

R

Spark

7+ years experience

SQL

Git/GitHub

AWS

GCP

Production ML

Education

BS/MS/PhD in Computer Science or related field

Tools

Git

Github

Job description

Intuit in Springfield, MO is seeking a Staff Machine Learning Engineer to collaborate with AI scientists to build and deploy scalable ML models. You will create data pipelines, train and productionize models, and evaluate ML solutions with modern tooling.

The role demands deep ML knowledge and cloud experience and strong communication across teams. Candidates should have advanced degrees and a track record delivering production systems, performance optimization, and end-to-end ML lifecycle in