Senior Machine Learning Engineer, Foundational Models and Agentic systems

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 elegant, state of the art technology which drives impact and creates awesome user experiences, 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
  
  

• 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
  
  

• 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