Sr Software Engineer, ML & MLOps

Overview

Join the innovative NLP Capabilities AI 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.

Join our brand-new innovative team to help build the next generation of awesome products and experiences using cutting-edge technology.

In This Role, You Will

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

• DevOps concepts (CI/CD)
• Software container technology (Docker, Kubernetes)
• Cloud technologies: AWS: storage, messaging, ML tools, KV storage networking
• Proven design and implementation experience in building complex ML pipelines
• Languages: Java, Scala, or Python (at least one at a high proficiency level)
• Software architecture patterns: microservices, CQRS, event sourcing.
• 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: SOLID, TDD, 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: SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras.
• Machine learning techniques (classification, regression, and clustering) and principles (training, validation, and testing)
• Data Processing tools: stream processing; Distributed computing systems and related technologies: Spark, Hive, or Flink
  
  

• 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