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# Senior Machine Learning Engineer

- **Company:** Intuit Inc.
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $171,000.0 - $231,500.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Systems Engineering, Big Data, Continuous Integration, Data Structures, DevOps, Distributed Systems, Github, Apache Hive, Python (Programming Language), Machine Learning, NLTK (NLP Analysis), NumPy, Tensorflow, Azure Machine Learning, Scala (Programming Language), Software Engineering, SQL Databases, Data Processing, Feature Engineering, Apache Spark, Keras, Git, Pandas, Build Management, Containerization, Scikit Learn, Kubernetes, Information Technology, Apache Flink, Production Code, Data Management, Stream Processing, Software Performance, Software Version Control, Docker
- **Published:** October 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ba2cfdc750e170ad

## About the Role

* BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.

 * Languages : Scala, Java , Python

 * Computer science fundamentals:

data structures, algorithms, performance complexity, and implications of computer architecture on software performance, e.g. I/O and memory tuning

 * Software engineering fundamentals:

version control systems (Git, Github) and workflows, and ability to write production-ready code.

 * Knowledge of Machine Learning or Data Science languages, tools, and frameworks: SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras.

 * Machine learning techniques (e.g. classification, regression, and clustering) and principles (e.g. training, validation, and testing)

 * Data Processing tools : stream processing Distributed computing systems and related technologies: Spark, Hive, Flink.

 * Cloud technologies - AWS

AWS Sagemaker tools

 * DevOps concepts, e.g. CI/CD

Software container technology, e.g. Docker, Kubernetes

## Description

In this role, you'll be part of a vibrant team of Data Scientists and Machine Learning engineers. You'll be expected to help architect, code, optimize, and deploy Machine Learning models at scale using the latest industry tools and techniques. You'll also help automate, deliver, monitor, and improve machine learning solutions. Important skills include software development, systems engineering, data wrangling, feature engineering, architecting, and testing., * Design and build systems which improve machine learning scalability, usability, and performance.

 * Work cross functionally with product managers, data scientists, and engineers to understand, implement, refine, and design machine learning and other algorithms.
 * Effectively communicate results to peers and leaders.
 * Explore the state-of-the-art technologies and apply them to deliver customer benefits.
 * Interact with a variety of data sources, working closely with peers and partners to refine features from the underlying data and build end-to-end pipelines

Use cases:

 * Model Productionalization: Work with data scientists to productionalize prototype models to the point where it can be used by customers at scale. This might involve increasing the amount of data used to train the model, automation of training and prediction, and orchestration of data for continuous prediction. The engineer would be expected to understand the details of the data being used and provide metrics to compare models.
 * Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time. In this use case you will need to understand the specifics of the algorithm implementation in order to enhance it. This enhancement could be exploratory work based off of a performance need or directed work based off of ideas that other data science team members propose.
 * Machine Learning Tools: The Big Data Engineer would build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one project. The goal of this type of use case would be to ease a pain point in the data science process. This may involve speeding up training, making a data processing easier, or data management tooling.