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

Qualcomm Technologies, Inc.; San Diego (CA); On-site; USD 141,000 - 211,000; Full time

Benefits offered by this job

Competitive annual discretionary bonus

Annual RSU grants

Comprehensive benefits package

Job summary

Qualcomm Technologies, Inc. is seeking a Senior Machine Learning Engineer to join its Autonomous Driving AI team.

The role centers on developing end-to-end autonomous driving systems that translate multimodal sensor inputs into safe driving behavior, covering training, simulation, and safety-critical evaluation. You will design, implement, and optimize state-of-the-art models, build scalable ML pipelines, and collaborate across perception, planning, simulation, and software teams to push the

Qualifications

• Degrees and experience in ML, AI, or robotics.
• Experience in ML, DL, CV, robotics, or autonomous driving.
• Strong foundations in software engineering and reproducible research.

Responsibilities

• Design and optimize end-to-end autonomous driving models.
• Build scalable ML pipelines for data processing, training, validation, deployment.
• Develop multimodal DL architectures using vision, map, route, and vehicle context.
• Design behavioral planning and trajectory generation models.
• Develop goal-conditioned driving systems and AI planning frameworks.
• Create robust evaluation methodologies for safety and driving performance.
• Collaborate with perception, planning, simulation, systems and software teams.

Skills

Deep learning

Multimodal learning

Reinforcement learning

Education

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field

Master’s degree in Computer Science, Engineering, Information Systems, or related field

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

Tools

Python

PyTorch

C++

ONNX

TensorRT

Transformers

Job description

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