We are seeking a Senior Machine Learning Engineer to join our end‑to‑end autonomous driving team! You will help build, train, and deploy large‑scale E2E driving models that leverage VLM/VLA architectures, and build a data flywheel that continuously improves our systems in the real world! Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
What you’ll be doing:
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.
What we need to see:
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.
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 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.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- PhD with 4+ years, MS with 6+ years, or BS with 8+ years of relevant experience in Computer Science, Computer Engineering, or related field
- Strong background in modern deep learning including transformer-based architectures and multimodal VLM/VLA models
- Hands-on experience training and deploying deep learning models on real-world datasets
- Practical experience with data-centric methods such as active learning or large-scale sample mining
- Proficiency in Python and at least one major deep learning framework
- Demonstrated ability to collaborate effectively across teams and lead projects
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
