Deep Learning Senior Engineer, End-To-End Autonomous Driving
At a glance
Senior Machine Learning Engineering role at NVIDIA. China, Shanghai · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Train generative, imitation and reinforcement-learning models that strengthen planning and reasoning in driving systems
- Prepare LLM, VLM and VLA models for real-world use in autonomous vehicles and robotics
- Develop data collection and generation approaches that improve training-set variety and quality
- Coordinate with other teams to launch models in production while meeting safety, reliability and performance standards
- Integrate machine-learning models with vehicle firmware for production software used in safety-critical settings
What you bring
- A PhD and at least two years of relevant experience, or a master’s degree or equivalent and at least four years
- Strong Python ability, experience with major deep-learning frameworks, and familiarity with C++ for model integration
- Deep knowledge of modern deep-learning architectures and optimization, plus production ML deployment in self-driving or related fields
- You have built LLMs, VLMs or VLAs from scratch, or are a proven coder with a strong interest in autonomous systems
Who this fits
This senior engineering role suits you if you bring production machine-learning experience and want to build systems for autonomous driving or robotics. You’ll work on safety-critical software that integrates models with vehicle firmware.
From the employer
At NVIDIA, we are seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy cutting-edge end-to-end autonomous driving systems, running on NVIDIA chips in mass-production vehicles. Our strategy has evolved from AI 1.0 — building a driver from scratch — to AI 2.0 — teaching an intelligent agent to drive. This next phase leverages LLMs, VLMs, and VLAs to bring unprecedented reasoning, planning capabilities, and interactivity with the driving system to autonomous vehicles and general robotics. Let’s build the future of autonomy—together!
What You’ll Be Doing:
- Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
- Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
- Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
- Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
- Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.
What We Need to See:
- Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems.
- Deep understanding of modern deep learning architectures and optimization techniques.
- Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.
- Strong programming skills in Python and proficiency with major deep learning frameworks.
- Familiarity with C++ for model deployment and integration in safety-critical systems.
- PhD with 2+ years, MS (or equivalent experience) with 4+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
Ways to Stand Out from the Crowd:
- Experience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics.
- Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems.
- Deep understanding of behavior and motion planning in real-world AV applications.
- Experience building and training large-scale datasets and models.
- Proven ability to optimize algorithms for real-time performance in resource-constrained environments and strong track record of taking projects from concept to production deployment.
#AutonomousVehicles
#deeplearning