Applied Scientist Ii, Foundation Model, Industrial Robotics Group

Amazon Amazon · Big Tech · Sunnyvale, CA · Machine Learning Science

The Applied Scientist II role focuses on developing and improving machine learning systems for industrial robotics, specifically leveraging and adapting foundation models for tasks like perception, reasoning, and action. This involves fine-tuning, optimization, experimentation, and building evaluation frameworks, with a contribution to data and training workflows. The goal is to enable generalization, multi-modal learning, and skill acquisition in robots operating at Amazon's scale.

What you'd actually do

  1. Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization.
  2. Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments.
  3. Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes.
  4. Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes.
  5. Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs

Skills

Required

  • PhD, or Master’s + 4+ years building ML models/algorithms in applied settings
  • 2+ years of building models for business application experience
  • Knowledge of programming languages such as C/C++, Python, Java or Perl
  • 2+ years hands-on experience in deep learning with strength in at least one: computer vision, multimodal models, imitation learning / RL for robotics, or human-robot interaction
  • Ability to design rigorous experiments, analyze results, and iterate quickly with reproducible baselines
  • Demonstrated technical contributions (e.g., publications, patents, open-source, or impactful internal systems)

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • building models for business application
  • deep learning
  • computer vision
  • multimodal models
  • imitation learning / RL for robotics
  • human-robot interaction
  • design rigorous experiments
  • analyze results
  • iterate quickly
  • reproducible baselines
  • technical contributions

Other signals

  • foundation models for robotics
  • multi-modal learning
  • skill acquisition
  • robot perception
  • environmental understanding