Senior Applied Scientist - Real-Time VIO & Sensor Fusion

Amazon

San Francisco (CA)

On-site

USD 183,800 - 248,700

Full time

14 days+
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Job summary

Amazon is seeking a Sr. Applied Scientist to develop and optimize Visual Inertial Odometry (VIO) and sensor fusion for intelligent robots.

You will design, implement, and deploy state estimation and tracking algorithms that enable robots to understand their position and motion in real time, even in challenging environments. This is a hands-on role spanning algorithm development to embedded deployment on resource-constrained hardware, with prototyping, testing, and iteration in real-world data.

Qualifications

  • PhD or MS with 6+ years of applied research in ML or robotics.
  • Experience building ML models for perception on embedded hardware.
  • Strong programming in C++, Python, and Java.

Responsibilities

  • Design and implement VIO algorithms for real-time state estimation on robotic platforms.
  • Develop multi-sensor fusion pipelines integrating cameras and IMUs.
  • Optimize perception and tracking for ARM and GPU-accelerated edge devices.
  • Apply ML techniques to complement geometric approaches for robustness.
  • Build calibration, evaluation, and benchmarking infrastructure.
  • Collaborate with hardware, controls, and navigation teams to integrate outputs.

Skills

VIO
Sensor fusion
Embedded systems
Machine learning
State estimation

Education

PhD in a relevant field
MS with 6+ years applied research

Tools

TensorFlow
PyTorch
Scikit-learn
C++
Java
Python

Job description

Amazon is seeking a Sr. Applied Scientist to develop and optimize Visual Inertial Odometry (VIO) and sensor fusion for intelligent robots.

You will design, implement, and deploy state estimation and tracking algorithms that enable robots to understand their position and motion in real time, even in challenging environments. This is a hands-on role spanning algorithm development to embedded deployment on resource-constrained hardware, with prototyping, testing, and iteration in real-world data.

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