Senior ML Engineer – Watson Orders


Austin, TX, USA Remote



Jan 25


Watson Orders is a IBM Silicon Valley based technology development group targeting the development of world-class conversational AI. Our mission is to deliver advanced technology solutions that address real-world, data driven needs in a customer-facing the quick service restaurant, environment. We are focused on using state-of-the-art Machine Learning, AI, and related technologies to completely transform the customer experience!

Your Role and Responsibilities

We are seeking to hire a Senior ML Engineer to join our growing team! This is a hands-on technical role where you will spend most of your time designing, coding, and implementing ML solutions to intent and understanding problems, along with ongoing evaluation of model performance both in isolation as well as using results from production use cases. We are looking for someone who has expertise designing and implementing ML training pipelines as we improve and scale our conversational AI productsAs a Senior ML Engineer, you are a ML specialist who will collaborate with other engineers to tackle complex algorithmic and architectural challenges. You will:

  • Investigate and experiment with new model architectures to improve existing ML models, training pipelines, and run-time inference performance
  • Creatively balance the demands of production-level software engineering with exploratory research and development
  • Work with teams to develop and improve ML pipelines that go all the way from R&D to at-scale deployment

Required Technical and Professional Expertise

  • Extensive experience working with large production codebases
  • Experience using PyTorch, MxNet, TensorFlow, or similar tools
  • Hands-on experience with ML model deployment and inference in a production environment
  • Experience with sequence-to-sequence neural networks (e.g. LSTMs, GRUs, Transformers, etc) in any domain

Preferred Technical and Professional Expertise

  • Experience developing production-level Python
  • Ability to build NLP data pipelines and perform data transformations to fit deep learning models for language learning or understanding
  • Experience with MLOps and/or ML at scale

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