Senior Machine Learning Engineer

Join Intuit in Mountain View, CA (onsite) and help bring machine learning from prototype to dependable, scalable production. This role focuses on architecting, coding, optimizing, and deploying ML models at scale, with an emphasis on automating delivery, monitoring outcomes, and continuously improving performance for customer impact.

Compensation: USD 171,000 - 231,500 per year.

What you’ll do

• Architect and build ML systems that improve scalability, usability, and performance.
• Collaborate cross functionally with product managers, data scientists, and engineers to understand needs, implement solutions, refine approaches, and design machine learning and related 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, partnering with others to refine features and build end-to-end pipelines.

How the work shows up

• Model productionalization: partner with data scientists to move prototype models toward customer-scale use. This can include increasing training data, automating training and prediction, and orchestrating pipelines for continuous prediction.
• Model comparison and metrics: apply understanding of the underlying data details and provide metrics to compare models.
• Model enhancement: improve existing codebases to raise prediction performance or reduce training time, including both exploratory work and directed improvements based on performance needs and ideas from data science teammates.
• Machine learning tools: build project-focused tools that reduce friction in the data science process, such as speeding up training, making data processing easier, or improving data management. These tools may span multiple projects and are generally decoupled from any single initiative.

Key skills and focus areas

You’ll be expected to help architect, code, optimize, and deploy ML models at scale using current industry tools and techniques. The role also includes automating, delivering, monitoring, and improving machine learning solutions, with strong emphasis on software development, systems engineering, data wrangling, feature engineering, architecting, and testing.

Requirements

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

Technologies you’ll work with

• Programming: Scala, Java, Python, SQL
• ML/DL: SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras
• Big data: Spark, Hive, Flink
• Cloud/ML ops: AWS, AWS Sagemaker, Docker, Kubernetes