Applied Machine Learning Engineer, Smart Devices (pico-lab) - San Jose

ByteDance ByteDance · Big Tech · San Jose, CA · R&D

Applied Machine Learning Engineer role focused on developing AI applications for next-generation XR smart devices (MR headsets, AR glasses, wearables). The role involves leading AI software prototyping, user studies, creating and deploying multimodal AI features, developing and maintaining ML models (leveraging open models and training new ones), designing evaluation frameworks, and staying updated on ML techniques. Requires a Master's or PhD in CS with 5+ years of ML infrastructure experience, including model deployment, evaluation, optimization, and data processing. Expertise in NLP, LLM, or Computer Vision is preferred.

What you'd actually do

  1. Develop AI applications for Smart Glasses, AR Glasses, MR Headsets and more smart wearables.
  2. Engage early on innovative product concepts in the Product Incubation Team. Lead AI software prototyping efforts and user studies. Create and deploy innovative multimodal AI features that redefine smart device experience.
  3. Develop and maintain Machine Learning (ML) models. Leverage open models as well as training new models. Design evaluation framework and carry out evaluations of the ML models.
  4. Stay up-to-date on the latest machine learning techniques and technologies and apply them to our technical solutions. Propose, design, and implement data collection solutions, collect and clean data to tune and evaluate machine learning models.

Skills

Required

  • Master or PhD in Computer Science or Equivalent
  • Experience with Machine Learning or Deep Learning
  • 5+ years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)

Nice to have

  • PhD in Computer Science
  • Experience with Mobile Development
  • Expertise in NLP, LLM, or Computer Vision
  • Excellent teamwork, problem-solving, and investigative skills

What the JD emphasized

  • 5+ years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Other signals

  • Develop AI applications for Smart Glasses, AR Glasses, MR Headsets and more smart wearables.
  • Create and deploy innovative multimodal AI features that redefine smart device experience.
  • Develop and maintain Machine Learning (ML) models. Leverage open models as well as training new models.