Senior ML Engineer: Scale & Productionize AI (Equity)

Intuit, Inc.; Mountain View (CA); On-site; USD 171,000 - 232,000; Full time

Job summary

Intuit, Inc. is seeking a data science/ML engineer to design and deploy scalable ML models in production. You will work with product managers, data scientists and engineers to implement and refine algorithms, communicate results to stakeholders, and explore state-of-the-art techniques for customer value.

You will handle data wrangling, feature engineering, and end-to-end model pipelines, contributing to productionalization and performance enhancements in a fast-paced environment.

Qualifications

• BS, MS, or PhD in Computer Science or related field, or equivalent practical experience.
• Languages: Scala, Java, Python.
• Strong CS fundamentals: data structures, algorithms, memory I/O considerations.
• Software engineering: Git/Github and production-ready code.
• Knowledge of ML/Data Science tools: TensorFlow, Keras, Scikit-Learn, NumPy, Pandas.
• ML techniques incl. classification, regression, clustering; training/validation/testing.
• Data processing tools: Spark, Hive, Flink; cloud: AWS/SageMaker; DevOps: CI/CD.
• Container tech: Docker, Kubernetes.

Responsibilities

• Design and build systems to improve ML scalability, usability, and performance.
• Collaborate with product managers, data scientists, and engineers to implement ML algorithms.
• Communicate results effectively to peers and leaders.
• Explore state-of-the-art tech and apply to customer benefits.
• Work with diverse data sources to refine features and build end-to-end pipelines.

Skills

Scala

Java

Python

Education

BS/MS/PhD in Computer Science or related field

Tools

Git/GitHub

Spark

Hive

Flink

TensorFlow

Keras

Scikit-Learn

NLTK

NumPy

Pandas

Job description

Intuit, Inc. is seeking a data science/ML engineer to design and deploy scalable ML models in production. You will work with product managers, data scientists and engineers to implement and refine algorithms, communicate results to stakeholders, and explore state-of-the-art techniques for customer value.

You will handle data wrangling, feature engineering, and end-to-end model pipelines, contributing to productionalization and performance enhancements in a fast-paced environment.