Staff Machine Learning Engineer

Key Skills

Algorithms, Amazon Simple Storage Service (S3), Apache Hive, AWS, Cloud computing, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Data structuring, DevOps, Docker, Effective communication skills, Git, Github, Java Programming, Kubernetes-K8s, Machine learning techniques, Natural Language Toolkit - NLTK, NumPy, Pandas, Python Programming, Scala Programming, Software Development, Spark Core, SQL, TensorFlow

Job Description

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 BU’s. We love engineers who lead the change, communicating with customers and delivering 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.

Qualifications

• Proven design and implementation experience in building complex ML
• Languages : Java, Scala , Python
• Software architecture patterns: microservices, CQRS, event sourcing .
• 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:
• 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,
• 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,
• Cloud technologies - AWS: Storage ,messaging, ML
• DevOps concepts, e.g. CI/CD
• Software container technology, g. Docker, Kubernetes

Responsibilities

• Design and build systems which improve machine learning scalability, usability, and
• 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
• 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 of a performance need or directed work based off of ideas that other data science team members
• 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 project. The goal of this type of use case would be to ease a pain point in the data science This may involve speeding up training, making data processing easier, or data management tooling.