Senior ML Systems Engineer - Vision & Perception

Shield AI

Boston (MA)

Presencial

USD 163.000 - 245.000

Jornada completa

14 días+

Una candidatura completa en un minuto

Currículum y carta de presentación personalizados, listos para enviar.

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Descripción de la vacante

Shield AI is seeking an experienced machine learning engineer to advance perception for autonomous systems. You will own major features from model development through deployment, working with researchers, perception engineers, autonomy engineers, and platform teams to deliver production-grade ML capabilities.

The role emphasizes vision, vision-language models, data pipelines, SFT workflows, and deployment for embedded hardware using ONNX, TensorRT, and related tools.

Formación

  • Proficiency in ML fundamentals and production CV deployment.
  • Experience with PyTorch and TensorFlow in a production setting.
  • Strong knowledge of 3D vision problems/algorithms.
  • Proficiency in C++ and Python for model development and deployment.
  • Ability to obtain a SECRET clearance.

Responsabilidades

  • Design, train, fine-tune, and maintain state-of-the-art vision models.
  • Build scalable data pipelines and supervised fine-tuning workflows.
  • Deploy and optimize models for embedded hardware using ONNX/TensorRT.
  • Apply ML techniques to perception and autonomy problems in real-world environments.
  • Collaborate with cross-functional teams to productionize ML capabilities.

Conocimientos

Python
C++
Machine learning fundamentals
3D vision
Analytical thinking

Herramientas

PyTorch
TensorFlow
ONNX
TensorRT

Descripción del empleo

Shield AI is seeking an experienced machine learning engineer to advance perception for autonomous systems. You will own major features from model development through deployment, working with researchers, perception engineers, autonomy engineers, and platform teams to deliver production-grade ML capabilities.

The role emphasizes vision, vision-language models, data pipelines, SFT workflows, and deployment for embedded hardware using ONNX, TensorRT, and related tools.

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