Overview

Join our mission

Join the Israeli MLE group as a Senior Machine Learning Engineer, developing the Intuit Security Risk and Fraud models. If you love having stretch goals, real world challenges, and making customers incredibly happy, while fostering your obsessive need for perfect code and user experience, this is the job for you.

Join our team to help build the next generation of awesome products and experiences using cutting edge technology. You will collaborate with many teams in Intuit and contribute to many components in different business units.

We love engineers who lead the change, communicate with customers, and deliver the most beautiful and intuitive applications.

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.

What you'll bring

• Computer science fundamentals:
• Proven design and implementation experience in building complex ML
• Languages : Java, Scala or Python
• Software architecture patterns: microservices, CQRS, event sourcing
• Data structures, algorithms, performance complexity, and implications of computer architecture on software performance, for example I/O and memory tuning
• Software engineering fundamentals:
• SOLID, TDD, 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 (for example classification, regression, and clustering) and principles (for example training, validation, and testing)
• Data Processing tools : stream processing
• Distributed computing systems and related technologies: Spark, Hive, Flink
• Familiar with
• Cloud technologies, in particular AWS.
• DevOps concepts (e.g., CICD).
• Software container technology (e.g., Docker)
  

How you will lead

• 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 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 a performance need, or directed work based off ideas that other data science team members propose
• Machine Learning Tools: The Machine learning Engineer would build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one 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 data processing easier, or data management tooling.