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

Join NVIDIA in Santa Clara, onsite, to lead the design, training, and deployment of end-to-end autonomous driving models. This senior role offers a salary range of USD 184,000 to 356,500 per year, plus equity and a comprehensive benefits package. You will collaborate across teams to build data-centric pipelines, drive data flywheels, and translate research into robust, production-grade machine learning systems for autonomous driving.

Benefits

• Equity
• Benefits

Responsibilities

• Design, implement, and train large-scale end-to-end driving models.
• Lead the data flywheel by identifying failure cases, specifying data collection and labeling needs, and iterating models to close real-world gaps in performance.
• Build, curate, and maintain high-quality multimodal datasets (video, sensor, language/action traces) tailored for end-to-end autonomous driving.
• Apply data-centric learning methods such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised approaches.
• Explore and productize new data sources including simulation, synthetic data, and world-model based generation to improve coverage and robustness.
• Design 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 models.

Requirements

• PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant work 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), along with solid software engineering practices (testing, code review, CI/CD).
• Proven ability to collaborate across teams, drive designs from prototype to production, and communicate clearly with technical and non-technical partners.
• A track record of leading complex cross-team projects, setting technical direction, and making critical decisions that impact multiple teams or products.

Technologies

• Python
• PyTorch
• TensorFlow
• JAX

Ways to stand out

• Experience building and operating data flywheels or large-scale ML data pipelines, 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 advanced data-centric ML methods, VLM/VLA, or autonomous driving through publications, open-source projects, or widely used internal tools.
• Background with safety, reliability, and validation requirements for autonomous driving or other safety-critical applications.