Principal Applied Scientist - Robotics

Oracle Oracle · Enterprise · Santa Clara, CA +1

This Principal Applied Scientist role focuses on delivering AI capabilities for robotic systems, involving applied research in multi-sensor fusion, perception, multimodal reasoning, and reinforcement learning, with a strong emphasis on full-stack execution from data to production deployment.

What you'd actually do

  1. Partner with science, engineering, product, and hardware leaders to identify strategic product needs where robotics, perception, and embodied AI can create measurable customer and business impact.
  2. Lead the design and execution of research programs and POCs for sensors, multi-sensor fusion, real-time signal processing, and perception systems.
  3. Lead full-stack execution across experimentation, data pipelines, model development, evaluation, deployment integration, and production monitoring.
  4. Demonstrate thought leadership in at least one business-critical area such as robot perception, multimodal systems, sensor fusion, reinforcement learning, or embodied AI.
  5. Mentor and guide scientists and engineers, raising the bar for applied research rigor, experimentation quality, and production readiness.

Skills

Required

  • machine learning
  • artificial intelligence
  • computer vision
  • perception
  • robotics
  • sensor fusion
  • real-time signal processing
  • object detection
  • tracking
  • activity recognition
  • scene understanding
  • localization
  • state estimation
  • multimodal AI systems
  • reinforcement learning
  • planning
  • sequential decision-making
  • robotics control interfaces
  • action-conditioned model behavior
  • Python
  • C++
  • modern ML frameworks
  • production-oriented software practices
  • APIs
  • SDKs
  • commercially viable robotics platforms

Nice to have

  • academic literature evaluation
  • industry benchmarks
  • robotics platforms
  • robot APIs
  • labeling approaches
  • simulation
  • synthetic data
  • model training
  • fine-tuning
  • optimization
  • inference design
  • compute/latency tradeoffs
  • deployment integration
  • production monitoring
  • code review
  • documentation
  • testing
  • delivery readiness
  • operational metrics
  • user feedback loops
  • root-cause analysis
  • patents
  • white papers
  • design documents
  • demos
  • conference-quality publications
  • external research groups
  • academic partners
  • commercial robotics ecosystem partners

What the JD emphasized

  • strong bias for hands-on execution and full-stack delivery
  • Bias for action with a strong hands-on orientation; able to move from ambiguous idea to prototype, evaluation, and production path quickly.
  • Ability to execute full-stack AI workflows spanning data, experimentation, modeling, evaluation, APIs, deployment, and feedback loops.

Other signals

  • delivering AI capabilities for commercially viable robotic systems
  • end-to-end solution development from data collection and experimentation through production deployment
  • full-stack delivery