Director, Deep Learning Solutions at NVIDIA

San Jose, California, United States

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Automotive, Robotics, GamingIndustries

Requirements

PhD or equivalent experience in Computer Science/Electrical Engineering
Minimum of 8 years of meaningful involvement in machine learning/deep learning research or practical experience
8+ years of leadership background
Overall 15+ years of industry experience
Over 10 years of validated expertise in the embedded software sector, holding technical leadership positions accountable for delivering outstanding production software within a multifaceted setting
Solid understanding of embedded operating system internals (QNX/Linux), memory management, C/C++, and embedded/system software concepts
Background in parallel programming, e.g., CUDA, OpenMP
Deep knowledge of GPU, CPU, and dedicated deep learning architecture fundamentals and low-level performance optimizations using heterogeneous computing
Hands-on with deep learning—comfortable reading/modeling code, not just running it
Strong intuition for modern architectures (e.g., transformers, diffusion)
Deep experience tuning for NVIDIA GPUs (kernels, memory, latency/efficiency trade-offs) / SOCs
Proven record delivering robust, low-latency inference at scale
Led teams that turn SOTA models into reliable, measurable business impact for embedded platforms
Ways to Stand Out
Leadership role in production deployment of Autonomous solutions for passenger cars, with deep understanding of constraints and advancements of sensing, computing, and model architecture evolutions
Lead teams that are located in various regions around the world
Experience with Automotive safety standards

Responsibilities

Drive Strategic Implementations of TensorRT inference solutions for Edge devices: Lead TensorRT releases and solutions for key verticals, including Game consoles, Robotics and Autonomous Vehicles for Jetson, DRIVE and GPU + x86 hardware platforms. Set up Proofs of Readiness (PORs) and guide their implementations
Coordinate the development and release of Torch-TRT and other alternative optimization frameworks like MLIR-TRT
Leading customer solutions: Collaborate with major automotive and robotics OEMs and Partners to adjust and optimize custom deep learning models for their specific requirements. Offer direct customer support, including debugging, technical education, and handling customer inquiries for our Automotive and Robotics partners. Responsible for drafting, negotiating, and finalizing SOWs with customers and partners
Performance Benchmarking: Orchestrate efforts to achieve leading performance results on industry benchmarks like MLPerf on various edge devices
Technical Leadership & Influence: Function as a technical leader for deep learning across multiple teams, giving oversight and build support. Apply customer insights to influence the composition and structure of upcoming SOC deep learning hardware
Scaling the team: Strategically hiring to meet new demands while also mentoring and adjusting existing teams to new deep learning challenges
Representing Nvidia Deep learning solutions in webinars, conferences and partner events

Skills

Key technologies and capabilities for this role

TensorRTDeep LearningTransformersDiffusion ModelsNVIDIA GPUsJetsonDRIVETorch-TRTMLIR-TRTLow-Latency InferenceGPU OptimizationPerformance Benchmarking

Questions & Answers

Common questions about this position

What experience level is required for this role?

A PhD or equivalent experience in Computer Science/Electrical Engineering is required, along with a minimum of 8 years in machine learning/deep learning, 8+ years of leadership, and overall 15+ years of industry experience, plus over 10 years in embedded software with technical leadership.

What key technical skills are needed for the Director, Deep Learning Solutions position?

Candidates need hands-on expertise with deep learning including modern architectures like transformers and diffusion, deep experience tuning for NVIDIA GPUs and SOCs, and a proven record delivering low-latency inference at scale for embedded platforms.

What is the team structure like for this role?

You will inherit a cohesive, high-performing team that’s been built and refined over the past eight years.

What does the role involve in terms of team leadership?

The role includes scaling the team by strategically hiring to meet new demands while mentoring and adjusting existing teams to new deep learning challenges.

Is this a remote position or does it require office work?

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.

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