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

NVIDIA in Santa Clara, CA is seeking a Senior Machine Learning Engineer to design, train, and deploy end-to-end autonomous driving models while building data-centric pipelines and data flywheels that accelerate learning from real-world data. This onsite role involves close collaboration across researchers and engineers to translate cutting-edge research into robust, production-ready ML systems for autonomous driving. The position offers a salary range of USD 184,000 to 356,500 per year and targets candidates with advanced degrees and substantial experience in modern deep learning and data-centric methods.

Responsibilities

• Designing, implementing, and training large-scale end-to-end driving models.
• Driving the data flywheel: identifying failure cases, specifying data collection and labeling needs, and iterating models to close real-world performance gaps.
• Building, curating, and maintaining high-quality multimodal datasets (e.g., video, sensor, language/action traces) tailored for end-to-end autonomous driving.
• Developing and applying data-centric learning algorithms such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised methods.
• Exploring and productizing new data sources including simulation, synthetic data, and world-model-based generation/augmentation to improve coverage and robustness.
• Designing and implementing agentic data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.
• Fostering collaborative partnerships with researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.

Requirements

• A PhD with 4+ years, an MS with 6+ years, or a 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.

Technologies

• Python
• PyTorch
• TensorFlow
• JAX

Benefits

• Equity
• Benefits

Ways to stand out from the crowd

• 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, such as impactful publications, open-source projects, or widely used internal tools.
• Background with safety, reliability, and validation requirements for autonomous driving or other safety-critical applications.