Sr SW Developer, ML & MLOps

Overview

Join the innovative Machine Learning Engineering group, to help build the next generation of awesome products and experiences using cutting edge technology.

If you love having stretch goals, real world 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 ML models at scale, using the latest industry tools and techniques
• Help automate, deliver, monitor, and improve ML solutions
  
  

• BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
• 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 (Git, Github) and workflows, and ability to write production-ready code.
• Knowledge of
• Machine Learning or Data Science languages, tools, and frameworks: Spark, Scala, Python, R, SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras, Java
• Machine learning techniques (e.g. classification, regression, and clustering) and principles (e.g. training, validation, and testing)
• Data query and data processing tools or systems: relational, NoSQL, stream processing
• Distributed computing systems and related technologies: Spark, Hive
• Mathematics fundamentals: linear algebra, calculus, probability
• Preferred Additional Qualifications:
• Familiarity with:
• Cloud technologies, in particular AWS.
• DevOps concepts, e.g. CI/CD
• Software container technology, e.g. Docker, Kubernetes
• Experience with designing and developing deep learning architectures
• Deploying highly scalable software for SaaS products
  

• Design and build systems which improve machine learning scalability, usability, and performance.
• Work cross functionally with product managers, data scientists, and engineers to understand, implement, refine, and design machine learning and other algorithms.
• 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