ML engineer

Introduction
WatsonX Orders is an 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 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 products. As a 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 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
• Deep understanding of Machine Learning mathematics and linear algebra

Preferred Technical and Professional Expertise

• Experience developing production-level Python
• Experience of working on NLP ASR or TTS models
• Experience with MLOps and/or ML at scale