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

NVIDIA Santa Clara, CA

Santa Clara, CA

Today

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

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Job Description

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.

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