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

NVIDIA in Santa Clara 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 development. This onsite role involves close collaboration with researchers and engineers to convert advanced research into robust, production-ready ML systems for autonomous vehicles.

Responsibilities

• Design, implement, and train scalable end-to-end driving models for autonomous vehicles.
• Drive the data flywheel by identifying failure modes, defining data collection and labeling needs, and iterating models to close real-world performance gaps.
• Build, curate, and maintain high-quality multimodal datasets including video, sensor streams, and language/action traces for end-to-end driving.
• Develop and apply data-centric learning strategies 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 or augmentation to improve coverage and robustness.
• Design and implement automated data workflows that handle discovery, labeling, evaluation, and retraining to maximize development velocity.
• Foster collaborative partnerships with researchers and engineers to transform innovative research into robust, production-ready ML models.

Requirements

• PhD with 4+ years, MS with 6+ years, or BS (or equivalent) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related field.
• Strong background in modern deep learning, including transformer architectures, video modeling, and multimodal VLM/VLA or foundation models.
• Hands-on experience training and deploying deep learning models on real-world datasets, covering 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 reviews, CI/CD).
• Demonstrated ability to collaborate across teams, drive designs from prototype to production, and communicate 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 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.