Senior Research Engineer - Autonomous Vehicles at NVIDIA

Santa Clara, California, United States

Apply Now

Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Autonomous Vehicles, Artificial IntelligenceIndustries

Requirements

Bachelor's degree in Computer Science, Robotics, Engineering, or a related field or equivalent experience
10+ years of full-time industry experience in large-scale MLOps and AI infrastructure
Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow
Deep familiarity with reinforcement learning algorithms like PPO, SAC, or Q-learning, including experience tuning hyperparameters and reward functions
Familiarity with common policy learning techniques like reward shaping, domain randomization, curriculum learning
Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like Kubernetes
Strong programming skills in Python and a high-performance language such as C++ for efficient system development
Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools (e.g., SLURM, Kubernetes)
Strong expertise in software engineering and in artificial intelligence topics, such as deep learning, reinforcement learning, and generative modeling
Strong programming skills
Solid track record of training deep learning models at scale
Good mathematical foundation to analyze new AI algorithms

Responsibilities

Develop large-scale supervised learning and reinforcement learning training frameworks to support multi-modal foundation models for AVs capable of running on thousands of GPUs
Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets
Implement scalable data loaders and preprocessors tailored for multimodal datasets, including videos, text, and sensor data
Build and optimize simulation infrastructure (based on GPU-accelerated simulators) to support the training of driving policies for AVs at scale
Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines
Develop sim-to-real transfer pipelines and work closely with the AV product team to deploy to real-world cars
Propose scalable solutions that combine LLMs with policy learning
Apply reinforcement learning to finetune multimodal LLMs
Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters

Skills

Key technologies and capabilities for this role

Deep LearningReinforcement LearningGenerative ModelingSupervised LearningGPU OptimizationCUDADistributed TrainingMulti-Modal ModelsSimulation InfrastructureData LoadersAutonomous DrivingSensor Data Processing

Questions & Answers

Common questions about this position

What experience level is required for this Senior Research Engineer role?

The role requires 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure.

What are the key technical skills needed for this position?

Candidates need proven experience with distributed training systems using PyTorch, JAX, or TensorFlow, deep familiarity with reinforcement learning algorithms like PPO, SAC, or Q-learning, and strong programming skills with a track record of training deep learning models at scale.

What is the educational requirement for this job?

A Bachelor's degree in Computer Science, Robotics, Engineering, or a related field, or equivalent experience is required.

What is the company culture like at NVIDIA for this team?

NVIDIA has an open and nurturing atmosphere for research that encourages collaboration, with opportunities to publish at top venues and work with the broader scientific community.

What salary or compensation does this position offer?

This information is not specified in the job description.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

Land your dream remote job 3x faster with AI

Try Jobo Free