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Staff Machine Learning Engineer

Job

NGV Talent

Brisbane, CA (In Person)

Full-Time

Posted 3 days ago (Updated 3 hours ago) • Actively hiring

Expires 7/1/2026

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Job Description

Staff Machine Learning Engineer at NGV Talent Staff Machine Learning Engineer at NGV Talent in BRISBANE, California Posted in 1 day ago.
Type:
full-time
Job Description:
NGV Talent works with an early-stage autonomous mobility series A startup focused on developing next-generation vehicle intelligence systems for safe and scalable transportation of goods. We are partnering with our client to support the search for a Staff-level Engineer to join their autonomy team. Key Responsibilities Research and develop new approaches using deep learning, neural networks, and large-scale foundation models for autonomous driving systems, including perception, prediction, planning, and control Work across the full machine learning lifecycle, from data analysis and model experimentation to evaluation and performance validation Contribute to end-to-end autonomy systems, including mapping, localization, and SLAM-based approaches Collaborate closely with simulation, product, and autonomy engineering teams to integrate ML models into broader system components Participate in cross-functional initiatives spanning perception, planning, and system-level autonomy development
Qualifications Required:
Advanced degree (Ph.D. or Master's) in Computer Science, Computer Engineering, Robotics, Mathematics, Physics, or a related field Strong foundation in machine learning and/or computer vision, with experience applying modern deep learning methods Hands-on experience with transformer-based architectures and state-of-the-art ML techniques Proficiency with PyTorch, TensorFlow, or similar ML frameworks Hands-on experience in 2D/3D object detection, segmentation, and multi-object tracking Familiarity with modern vision architectures such as DETR, BEVFormer, Vision Transformers (ViT), or similar transformer-based models Experience with 3D perception, including BEV-based methods and multi-view geometry Solid background in deep stereo depth estimation or related depth perception techniques Ability to work independently in a fast-paced environment while collaborating across teams
Preferred:
Publications in top-tier ML, robotics, or computer vision conferences (first-author preferred) Experience with generative models, knowledge distillation, or model inference optimization techniques (e.g., TensorRT)