Staff Machine Learning Engineer

Come join the Fintech Risk AI/ML Smart Money Services (SMS) as a Senior Machine Learning Engineer. The SMS team is responsible for detecting and preventing fraud and financial risk for all Money Movement events at Intuit

Responsibilities

As a Staff MLE, you will bridge the gap between advanced data science and scalable production engineering:

• Lead the conception, coding, and deployment of data science models at scale using the latest industry tools. * Provide guidance and support leadership to business leaders and stakeholders on how best to harness available data for critical business needs.

• Help develop, maintain, and scale web services for the AI team which orchestrate various ML functions.

• Apply proven methods and hacking skills to work with divergent data types, data scales, and big data (petabytes).

• Partner closely with data scientists to understand, implement, and refine algorithms, building robust pipelines to train and deploy models.

• Discover, extract, and clean data from complex sources, making it machine learning-ready for large-scale analysis.

• Run regular A/B tests, gather data, draw conclusions on the impact of your models, and communicate results to peers, cross-functional partners, and leaders.

• Explore new technology shifts to determine how they might connect with customer benefits.

Qualifications

• BS, MS, or PhD degree in Computer Science, Statistics, Applied Mathematics, or a related field.

• 8+ years of experience developing predictive models, statistical analysis, or equivalent software engineering experience.

• Strong proficiency and experience with Object-Oriented Languages (OOD) such as Python, Scala, or Java.

• Deep knowledge of Data Science tools and frameworks (e.g., Python, Scikit, Pandas, TensorFlow, Keras, Spark).

• Strong software engineering fundamentals: data structures, algorithms, performance complexity, version control systems (Git), and the ability to write production-ready code.

• Demonstrated experience with web services (e.g., REST) and API design.

• Experience integrating applications and platforms with cloud technologies (e.g., AWS) and deploying highly scalable software supporting millions or more users.

• Practical experience with advanced concepts such as Supervised and Unsupervised ML techniques, Generative AI (autoencoders, GANs, VAEs), and GPU acceleration (CUDA and cuDNN).

• Excellent communication skills, including the ability to conduct meetings, make professional presentations, and explain complex technical concepts to non-technical business stakeholders.