Intuit
AI Scientist 2
IntuitIndia2 days ago
Full-timeEngineering, Information Technology
Overview

We are looking for an experienced Machine Learning Engineer to join Intuit’s AI, Research, and Futures organization to build AI/Machine Learning solutions that leverage emerging new technologies and capabilities and unlock entirely new AI-driven experiences for our customers. You will be part of a vibrant team of AI Scientists and MLEs and lead efforts to translate cutting-edge research into robust, scalable AI systems that deliver measurable customer impact.

What you'll bring

  • MS, or PhD in an appropriate technology fi eld (Computer Science, Statistics, Applied Math, Operations Research, etc.) or equivalent work experience.
  • 1+ years of experience in modern AI/ML tools and profi cient in Python and typical AI/ML libraries (e.g., TensorFlow, PyTorch, Keras). Familiarity with distributed computing frameworks (e.g., Spark, Ray) is a plus.
  • 1+ years of experience in machine learning techniques such as classifi cation, regression, neural networks, large language models, recommender systems, natural language processing, clustering, anomaly detection, and computer vision. Understanding of MLOps principles and practices (e.g., version control, CI/CD for ML models) is a plus.
  • Familiar with the latest trends and applications of generative AI including agentic applications.
  • Effi cient in SQL
  • Comfortable in a Linux environment
  • Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences

How you will lead

  • Model Prototyping: The ML Engineer would be expected to build prototype models alongside data scientists. This may involve data exploration, high-performance data processing, and machine learning algorithm exploration. The ML engineer will be expected to come up with a rationale for model choice and come up with metrics to properly evaluate models.
  • Model Productionalization: Works with data scientists to productionalize prototype models to the point where it can be used by customers at scale. This might involve increasing the amount of data used to train the model, automation of training and prediction, and orchestration of data for continuous prediction. The engineer would be expected to understand the details of the data being used and provide metrics to compare models.
  • Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time. In this case you will need to understand the specifics of the algorithm implementation in order to enhance it. This enhancement could be exploratory work based off a performance need or directed work based off of ideas that other data science team members propose.
  • Machine Learning Tools: The ML Engineer would build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one project. The goal of this type of use case would be to ease a pain point in the data science process. This may involve speeding up training, making a data processing easier, or data management tooling.

Key Skills

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