Senior Applied Scientist - Real-Time VIO & Sensor Fusion

Amazon; San Francisco (CA); Presencial; USD 183.800 - 248.700; Jornada completa

Descripción de la vacante

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.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

VIO

Sensor fusion

Embedded systems

Machine learning

State estimation

Educación

PhD in a relevant field

MS with 6+ years applied research

Herramientas

TensorFlow

PyTorch

Scikit-learn

C++

Java

Python

Descripción del empleo

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.