Staff Machine Learning Engineer

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)