Senior Machine Learning Engineer – End-to-End Autonomous Driving
- Compensation
- Not disclosed
- Location
- San Diego, California, United States of America
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Ml / AI
- Posted
Skills
Nice to have
- Master's or Ph.D. in Computer Science, Electrical Engineering, Robotics, Machine Learning, Artificial Intelligence, or a related field.
- 5+ years of industry or research experience in machine learning, deep learning, computer vision, robotics, or autonomous driving.
- Strong programming skills in: PythonPyTorchC++ (preferred)
- Deep understanding of: Deep LearningMachine LearningNeural Network DesignComputer VisionTransformersRepresentation LearningMultimodal Learning
- Experience training and evaluating large-scale deep learning models.
- Experience building end-to-end ML 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.
Full description
##
Company:
Qualcomm Technologies, Inc.
Job Area:
Engineering Group, Engineering Group > Machine Learning Engineering
General Summary:
About the Role
We are seeking a highly motivated Senior Machine Learning Engineer to join our Autonomous Driving AI team and help build the next generation of end-to-end driving intelligence. In this role, you will develop advanced deep learning systems that directly map multimodal sensor inputs into safe and reliable driving behavior.
You will work at the intersection of machine learning, autonomous driving, behavioral planning, large-scale training infrastructure, simulation, and safety-critical evaluation. The ideal candidate is passionate about building production-grade AI systems and advancing the state of the art in autonomous vehicle technology.
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 that leverage 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 for measuring model quality, safety, and driving performance.
- Develop simulation-based validation and closed-loop evaluation frameworks.
- Investigate failure cases and long-tail scenarios to improve model robustness and generalization.
- Design data augmentation, synthetic data generation, curriculum learning, and self-training approaches.
- Collaborate closely 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.
Minimum Qualifications:
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 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 and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field.