Full-Time

Senior Software Engineer

Deep Learning Inference, Automotive Safety

Posted on 6/13/2026

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$152k - $241.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

Hybrid

Category
AI & Machine Learning (1)
Required Skills
Software Testing
Neural Networks
C/C++
Requirements
  • Bachelor's, Master's, PhD, or equivalent experience in Computer Engineering, Computer Science, Electrical Engineering, AI
  • At least 5+ years of relevant software development experience
  • Strong C++ skills, including knowledge of and application of best practices with C++14 or newer standards
  • Familiarity with deep learning concepts and frameworks
  • Experience with safety-critical software development, including rigorous testing, validation, and documentation practices
  • A track record of taking initiative and driving projects to completion
  • Excellent interpersonal skills and a collaborative, pragmatic approach to solving problems
Responsibilities
  • Lead the design and development of high-performance deep learning inference software for safety-critical automotive applications using modern C++
  • Orchestrate the integration of new hardware functionalities into TensorRT's compiler and runtime for specialized and constrained platforms
  • Work closely with teams and stakeholders across the hardware and software stack to understand and leverage new technologies to improve TensorRT's functionality and performance
  • Guide the design and implementation of robust, high-quality C++ code in alignment with Modern C++ standards and safety-critical software requirements
  • Drive systematic development of test plans from unit to integration level, with emphasis on rigorous safety validation and verification
  • Lead documentation efforts for safety-critical properties of functions, classes, and systems to support certification and robustness requirements
  • Contribute to performance optimization and benchmarking efforts for specialized automotive platforms
Desired Qualifications
  • Experience with automotive safety standards (e.g., ISO 26262, ASIL) or other functional safety frameworks
  • Proficiency with Python and/or CUDA, ideally with experience in a professional environment
  • Background with systems programming, embedded systems, and/or compiler development
  • Experience in software performance benchmarking, profiling, and optimizations
  • Experience with state-of-the-art deep learning models (such as Large Language Models) and frameworks for inference

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Your Connections

People at NVIDIA who can refer or advise you

Simplify Jobs

Simplify's Take

What believers are saying

  • Blackwell and AI factory demand keep NVIDIA tied to hyperscaler capital spending.
  • Optical interconnects and networking expand NVIDIA beyond GPUs into rack-scale infrastructure.
  • Robotics, XR, and autonomous vehicles open new enterprise software and hardware revenue streams.

What critics are saying

  • Custom silicon from hyperscalers steadily erodes NVIDIA's GPU pricing power.
  • Power shortages and grid constraints delay AI data-center deployments worldwide.
  • Enterprise AI standards from Cisco, Equinix, and partners commoditize NVIDIA's reference architectures.

What makes NVIDIA unique

  • NVIDIA dominates AI GPUs and accelerated computing across gaming, data centers, and robotics.
  • CUDA creates high switching costs by binding developers to NVIDIA's software ecosystem.
  • NVIDIA now sells full-stack AI factories, not just chips, systems, and software.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-3%

2 year growth

-3%
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