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9d ago

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

NVIDIA Santa Clara, CA

Santa Clara, CA

9d ago

$184k - $357k/yr Full time Senior Active Learning Artificial Intelligence

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Job Description

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.

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