Senior Machine Learning Engineer – End-to-End Autonomous Driving

Qualcomm is seeking a Senior Machine Learning Engineer to join its Autonomous Driving AI team and help build next-generation end-to-end driving intelligence. The role focuses on deep learning systems that translate multimodal sensor inputs into safe, reliable driving behavior, covering training infrastructure, simulation, and safety-critical evaluation.

Responsibilities

• Design, develop, and optimize state-of-the-art end-to-end autonomous driving models.
• Build scalable machine learning pipelines for data processing, training, validation, and deployment.
• Develop multimodal deep learning architectures leveraging vision, map, route, and vehicle context information.
• Design and implement behavioral planning and trajectory generation models.
• Develop goal-conditioned driving systems and controllable AI planning frameworks.
• Create robust evaluation methodologies and metrics to measure model quality, safety, and driving performance.
• Develop simulation-based validation and closed-loop evaluation frameworks.
• Investigate failure cases and long-tail scenarios to improve robustness and generalization.
• Design approaches for data augmentation, synthetic data generation, curriculum learning, and self-training.
• Collaborate with perception, planning, simulation, systems, and software engineering teams.
• Drive innovation through new algorithms, technical leadership, publications, and patent generation.
• Contribute to production deployment and optimization of machine learning models on automotive platforms.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field, plus 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• OR Master’s degree in Computer Science, Engineering, Information Systems, or related field, plus 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• OR PhD in Computer Science, Engineering, Information Systems, or related field.
• Master’s or PhD in Computer Science, Electrical Engineering, Robotics, Machine Learning, Artificial Intelligence, or related field.
• 5+ years of industry or research experience in machine learning, deep learning, computer vision, robotics, or autonomous driving.
• Strong programming skills in Python, PyTorch, and C++ (preferred).
• Deep understanding of deep learning, machine learning, neural network design, computer vision, transformers, representation learning, and multimodal learning.
• Experience training and evaluating large-scale deep learning models.
• Experience building end-to-end machine learning workflows from data ingestion through deployment.
• Strong software engineering fundamentals including code quality, version control, testing, and reproducible experimentation.
• Experience in autonomous driving, ADAS, robotics, or embodied AI.
• Experience with end-to-end planners and AI planning systems.
• Experience with trajectory prediction, motion planning, or behavior modeling.
• Experience with reinforcement learning, imitation learning, or self-supervised learning.
• Experience building simulation and closed-loop evaluation frameworks.
• Familiarity with ONNX, TensorRT, distributed training, and model optimization techniques.
• Publications at top-tier conferences such as CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, or CoRL.
• Experience filing patents or developing novel machine learning technologies.

Technologies

• Python
• PyTorch
• C++
• ONNX
• TensorRT
• Transformers

Location and Compensation

Location: San Diego, CA (onsite)

Salary: USD 140,800 - 211,200 per year

Pay range: $140,800.00 - $211,200.00

Benefits

• Competitive annual discretionary bonus program
• Opportunity for annual RSU grants
• Highly competitive benefits package

Job Area

Engineering Group > Machine Learning Engineering

What You’ll Work On

• End-to-End Autonomous Driving Systems
• Vision-Language-Action (VLA) Models
• AI-Based Driving Planners
• Goal-Conditioned Driving Policies
• Large-Scale Deep Learning Training Infrastructure
• Trajectory Prediction and Vehicle Behavior Modeling
• Closed-Loop Evaluation and Simulation
• Safety-Critical Validation Pipelines
• Data-Centric AI and Long-Tail Learning
• Autonomous Vehicle Foundation Models