Staff Machine Learning Engineer, Foundation - Seattle

Qualtrics Qualtrics · Seattle · Seattle, WA · Engineering - Foundation

Staff Machine Learning Engineer focused on building and scaling enterprise AI/ML infrastructure and platforms for Qualtrics. The role involves designing robust AI architectures, optimizing ML systems for performance and cost, and leading the creation of production deployment and monitoring platforms. It requires collaboration with research scientists and product managers, and offers technical supervision to ML platform teams.

What you'd actually do

  1. Design and develop robust AI architectures, frameworks, and algorithms that can support large-scale and complex enterprise SaaS AI solutions in alignment with business objectives.
  2. Evaluate and select appropriate AI technologies, tools, and frameworks to achieve desired performance, accuracy, and scalability.
  3. Collaborate across the company to guide the direction of machine learning at Qualtrics, spanning teams from research to production.
  4. Communicate with a team of research scientists, product managers, and engineers and lead and document AI architectures, design decisions, and technical specifications for reference and knowledge sharing.
  5. Work closely with research scientists and model engineering teams on developing, and deploying, ML systems in production. Develop a strategy for optimizing models and systems for performance, scalability, efficiency, and cost.

Skills

Required

  • Python
  • Java
  • C++
  • TensorFlow
  • PyTorch
  • Keras
  • AWS
  • Azure
  • Google Cloud
  • Machine Learning Engineer
  • AI architectures
  • ML infrastructure
  • scalability
  • performance optimization

Nice to have

  • DeepSpeed
  • Accelerate
  • FasterTransformer
  • Transformers-NeuronX
  • ethical considerations
  • responsible AI practices

What the JD emphasized

  • proven experience (7+ years)
  • lead the creation of future ML platform
  • optimize models and systems for performance, scalability, efficiency, and cost

Other signals

  • Develop creative solutions to improve our ML infrastructure and scalability
  • Design and develop robust AI architectures, frameworks, and algorithms that can support large-scale and complex enterprise SaaS AI solutions
  • Develop a strategy for optimizing models and systems for performance, scalability, efficiency, and cost
  • Offer technical supervision and direction to the ML platform teams and lead the creation of future ML platform for deploying and monitoring AI models in production settings