Staff Machine Learning Engineer

Company Overview

Job Overview

In its intrapreneurial function, the AI Futures team develops and incubates emerging technologies, user experiences, capabilities and products that have the potential to be transformative to businesses and consumers. The mission of the AI Research team is to do cutting edge research in AI topics that directly influence Intuit’s portfolio of fintech offerings and position Intuit as a global leader in AI with the goal of powering prosperity around the world. In this role, you will be advancing the state of the art in fundamentals of artificial intelligence. We are seeking candidates who are experts in generative AI, fundamentals of deep learning and optimization, and passionate about areas such as neuro-symbolic AI, personalization and recommendation, theory and applications of large language models, and robustness and uncertainty quantification. You will join a high-caliber team of domain experts, data scientists, and machine learning engineers to shape our strategy in this area, and develop advanced capabilities that serve our customers’ needs, both current and future.

Responsibilities

• Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
• Build "machine learning ready" feature pipelines.
• Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
• Run regular A/B tests, gather data, and draw conclusions on the impact of your models.
• Monitor and maintain production models.
• Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
• Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.

Qualifications

• BS, MS, or PhD degree in Computer Science or related field, or equivalent work experience.
• 6+ years of experience
• Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
• Knowledge of machine learning techniques (i.e. classification, regression, and clustering).
• Understand machine learning principles (training, validation, etc.)
• Knowledge of data query and data processing tools (i.e. SQL)
• 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: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code.
• Experience deploying highly scalable software supporting millions or more users
• Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)