Senior Machine Learning Engineer, Perception – Autonomous Driving

This is a fully remote position, open to applicants in California.

📋 Description

• Create comprehensive solutions for perception and autonomous vehicle systems to facilitate traffic signal detection in various driving conditions, such as intricate intersections, rural pathways, and highways.

• Engage in applied research and development of cutting-edge deep learning models for tasks including traffic light detection, traffic sign recognition, road marking detection, construction object detection, text recognition, and other related traffic signal functions.

• Formulate adaptable strategies to accommodate various Operational Design Domains (ODDs) and support expansion across different countries and regions.

• Lead and prioritize data-driven initiatives in collaboration with extensive data collection and labeling teams.

• Organize data collection and labeling priorities while enhancing labeling efficiency to maximize data utility.

• Utilize data simulation and augmentation techniques to address extreme scenarios.

• Transform perception solutions into market-ready products by fulfilling requirements for safety, latency, and software reliability.

⛳️ Requirements

• Minimum Qualification: PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of pertinent experience in Computer Science, Computer Engineering, or a related technical discipline.

• A minimum of 2 years in a technical leadership role showcasing significant technical and organizational complexity is highly advantageous.

• Practical experience in developing deep learning algorithms aimed at solving complex real-world challenges.

• Proficiency in deep learning frameworks (e.g., PyTorch).

• Experience in data-driven development and collaboration with data and ground truth teams.

• Strong programming abilities in Python and/or C++.

• Exceptional communication and teamwork capabilities.

• Demonstrated expertise in creating generalizable perception solutions for autonomous driving or robotics utilizing deep learning with camera systems.

• Practical experience in developing and implementing DNN-based solutions on embedded platforms for real-time applications.

• Established expertise in deep learning supported by technical publications in esteemed conferences and journals.

• Knowledge of Visual Language Models, Transformers, BEV architectures, and contemporary traffic signal perception methodologies.

• Experience tackling complex object detection and recognition challenges is a significant plus.

🏝️ Benefits

• Equity

• Benefits