NVIDIA

Senior Machine Learning Engineer- Automotive

Rol remoto de Machine Learning Engineering con fit claro de ubicación del candidato.

Publicado18 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Austria

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception (e.g., object detection/classification, semantic segmentation, tracking).
Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting.
Productization: Take ML models and algorithms from initial evaluation and experimentation all the way to product level on the NVIDIA DRIVE AV platform, developing highly efficient product code in C++.
Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance.
Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.

MS in Computer Science, Engineering, or a related field, or equivalent experience.
6+ years of relevant proven industry experience applying machine learning to address real-world problems.
Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.
Deep practical experience applying machine learning to lidar/camera perception in automotive or related fields.
Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of the mathematical foundations of ML.
Building and sustaining training and essential metric workflows for large-scale datasets.
Excellent communication and analytical skills. Self-motivated drive to solve hard problems.

LiDAR or Camera Perception Experience: Proven track record of developing and shipping deep learning models for LiDAR/Camera in a production environment.
Advanced Model Knowledge: Familiarity with modern network architectures like Transformers and their application to visual recognition tasks.
AV Production Experience: A history of delivering ML features and models into a production autonomous vehicle stack or a related robotics product.
Performance Optimization: Experience with model optimization for real-time inference on embedded or automotive platforms (e.g., using TensorRT).

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