Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

Join the innovative Generative AI group, to help build the next generation of awesome products and experiences using cutting-edge technology.

If you love having stretch goals, challenges, and making customers incredibly happy while fostering your obsessive need for perfect code and user experience, this is the job for you.

You will collaborate with many teams in Intuit and contribute to many components in different business units. We love engineers who lead the change, communicate with customers, and deliver the most beautiful and intuitive applications.

In this role, you’ll:

Be part of a vibrant team of Data Scientists and ML Engineers
Be expected to help code, optimize, and deploy GenAI models at scale, using the latest industry tools and techniques
Help automate, deliver, monitor, and improve GenAI solutions

#LI-Hybrid

Responsibilities

Design and build systems, which improve Generative AI inference, quantization, optimization, finetuning, and evaluation
Work cross-functionally with product managers, data scientists, and engineers to understand, implement, reﬁne, and design Generative AI models
Effectively communicate results to peers and leaders
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

Qualifications

LLM experience: LangChain, vLLM, HuggingFace toolkit
Machine Learning oriented languages, tools, and frameworks: Spark, Python
Cloud technologies, in particular AWS, and Software container technology: Docker, Kubernetes, KubeFlow / MLflow
Experience with designing and developing Generative AI architectures
Machine learning techniques (classiﬁcation, regression, and clustering) and principles (training, validation, and testing)
Data query and data processing tools or systems: relational, NoSQL, stream processing
Distributed computing systems and related technologies: Spark, Hive
Software engineering fundamentals: version control systems (Git, Github) and workﬂows, and ability to write production-ready code
Computer science fundamentals: data structures, algorithms, performance, complexity, and implications of computer architecture on software performance (I/O and memory tuning)
Mathematics fundamentals: linear algebra, calculus, probability
BS, MS, or PhD degree in Computer Science or related ﬁeld, or equivalent practical experience