Senior Machine Learning Engineer

Intuit is seeking a Senior Machine Learning Engineer in Mountain View, CA (onsite) to architect, build, optimize, and deploy machine learning models at scale. The role centers on productionizing ML solutions, improving performance and usability, and partnering across teams to deliver customer value.

Responsibilities

• Design and build systems that improve machine learning scalability, usability, and performance.
• Collaborate with product managers, data scientists, and engineers to understand, implement, refine, and design machine learning and other algorithms.
• Communicate results effectively to peers and leaders.
• Evaluate state-of-the-art technologies and apply them to deliver customer benefits.
• Work with a variety of data sources and partner closely to refine features from underlying data and build end-to-end pipelines.

Use Cases

• Model Productionalization: Work with data scientists to move prototype models toward customer-ready deployment at scale.
• Support productionalization efforts that may include increasing training data volume, automating training and prediction, and orchestrating continuous prediction pipelines.
• Understand the details of training data and provide metrics to compare models.
• Model Enhancement: Improve prediction performance or reduce training time in existing codebases.
• Enhancement work may include exploratory investigations based on performance needs or directed efforts based on ideas proposed by data science team members.
• Machine Learning Tools: Build tooling for specific projects or multiple decoupled projects to reduce friction in the data science process.
• Tooling goals can include speeding up training, simplifying data processing, or improving data management.

Overview

You will be expected to help architect, code, optimize, and deploy machine learning models at scale using current industry tools and techniques. The role also includes automating, delivering, monitoring, and improving machine learning solutions. Key focus areas include software development, systems engineering, data wrangling, feature engineering, architecting, and testing.

Requirements

• BS, MS, or PhD degree in Computer Science or a related field (or equivalent practical experience).

Technologies

• Languages: Scala, Java, Python
• ML/Data: SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras
• Big data/Streaming: Spark, Hive, Flink
• Cloud/ML platforms: AWS, AWS Sagemaker
• Containers/Orchestration: Docker, Kubernetes

Additional Qualifications

• Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (including I/O and memory tuning).
• Software engineering fundamentals: version control systems (Git, Github), workflows, and the ability to write production-ready code.
• Machine learning or data science languages, tools, and frameworks: SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras.
• Machine learning techniques and principles, including classification, regression, clustering, and training, validation, and testing.
• Data processing tools and distributed systems: Spark, Hive, Flink.
• Cloud technology: AWS and AWS Sagemaker.
• DevOps concepts such as CI/CD.
• Software container technology such as Docker and Kubernetes.

Location and Compensation

Location: Mountain View, CA (onsite).
Salary: USD 171,000 - 231,500 per year.