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

NVIDIA•Us, CA

19h

About The Position

We are seeking a Senior Machine Learning Engineer to join our end-to-end autonomous driving team. This role involves building, training, and deploying large-scale E2E driving models utilizing VLM/VLA architectures, and establishing a data flywheel for continuous system improvement in real-world scenarios. The company is leveraging AI to advance computing, with GPUs serving as the intelligence for computers, robots, and self-driving cars. The position offers an opportunity to innovate and make a significant impact within a diverse and supportive environment.

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 at least some 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 effectively 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.

Nice To Haves

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.

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.
Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.