Senior Software Engineer

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 The Intuit Foresight team builds products that accelerate the adoption of new technologies and seed new businesses fueled by technology innovation . You’ll be building and prototyping applications to simplify the business of life for 60 million consumers and small businesses. Applications will span multiple business lines, including personal finance, small business accounting, and tax. You will thrive on ambiguity and experimentation while working closely with AI Scientists building the next generation of AI algorithms. You will be part of a small but high velocity team where team members frequently wear multiple hats as they build new products. The position involves close collaboration with our product, AI and design teams, as well as product teams in business units. Responsibilities Responsible for design of common components/frameworks/models that assist in building AI native apps, Fullstack LLM apps. Own end to end development of frameworks/models/ ML pipelines that fit use cases by working with the consuming teams and dependencies. Being able to navigate through ambiguity, lead with clarity and PoC Rapid prototyping, experimentation and iterations to build/own high accuracy/performant frameworks/models. Self organized, explore new shifts in GenAI/AI and look at possible application/improvements in the existing use cases. Qualifications BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience with 5+ years of experience in building/designing AI/ML applications.  Well versed in building with Python, PyTorch, Numpy, Pandas, TensorFlow  Machine learning fundamentals for supervised, unsupervised & reinforcement learning (i.e. classification, regression, clustering, neural networks) and experience in building production grade models with precision/recall.  Understand and apply machine learning principles (training, weights, validation, testing, error, cost) optimizing for accuracy/feedback. Should establish and own metrics/optimization. Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning). Experience with integrating applications and platforms with cloud technologies (e.g: AWS Sagemaker) Understanding of LLM, LangChain, CustomGPTs, Prompt Management, and ability to fine-tune base models to build efficient production-grade LLM apps Footer Intuit