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

Details

We are seeking a Senior Machine Learning Engineer to join our end-to-end autonomous driving team. You will help build, train, and deploy large-scale E2E driving models that leverage VLM/VLA architectures, and build a data flywheel that continuously improves our systems in the real world. Today, we’re tapping into the unlimited potential of AI to define the next era of computing — an era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world.

Responsibilities

• Design, implement, and train large-scale end-to-end driving models.
• Drive the data flywheel: identify failure cases, specify data collection and labeling needs, and iterate models to close real-world performance gaps.
• Build, curate, and maintain high-quality multimodal datasets (e.g., video, sensor, language/action traces) tailored for end-to-end autonomous driving.
• Develop and apply data-centric learning algorithms such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised methods.
• Explore and productize new data sources including simulation, synthetic data, and world-model-based generation/augmentation to improve coverage and robustness.
• Design and implement agentic data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.
• Foster collaborative partnerships with researchers and engineers to transform innovative research into robust, industrial-strength machine learning models.

Requirements

• PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
• Strong background in modern deep learning, including transformer-based architectures, video modeling, and multimodal VLM/VLA or foundation models.
• Hands-on experience training and deploying deep learning models on real-world datasets: data preprocessing, distributed training, evaluation, debugging, and iterative improvement.
• Practical experience with data-centric methods such as active learning, curriculum learning, outlier/novelty detection, or large-scale sample mining.
• Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code review, CI/CD).
• Demonstrated ability to collaborate across teams, drive designs from prototype to production, and communicate clearly with technical and non-technical partners.
• Track record of leading complex cross-team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.

Ways to stand out

• Experience building and operating data flywheels or large-scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
• Direct experience with end-to-end driving models, large-scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
• Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
• Contributions to sophisticated methods in data-centric ML, VLM/VLA, or autonomous driving (publications, open-source projects, or widely used internal tools).
• Background with safety, reliability, and validation requirements for autonomous driving or other safety-critical applications.

Compensation & Benefits

• Base salary range by level: Level 4 — 184,000 USD to 287,500 USD; Level 5 — 224,000 USD to 356,500 USD. Base salary is determined based on location, experience, and pay of employees in similar positions.
• Eligible for equity and benefits (link provided in original posting).

Additional information

• Applications accepted at least until April 25, 2026. This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is an equal opportunity employer and committed to fostering a diverse work environment.