Full-Time

Deep Learning Algorithm Engineer – New College Graduate 2025

Posted on 9/30/2025

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$148k - $235.8k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

In Person

Category
AI & Machine Learning (1)
Required Skills
LLM
Python
Tensorflow
Pytorch
Machine Learning
C/C++
Requirements
  • Pursuing or recently completed a Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience)
  • Experience in deep learning or applied machine learning
  • Strong foundation in deep learning algorithms, including hands-on experience with LLMs and VLMs
  • Deep understanding of transformer architectures, attention mechanisms, and inference bottlenecks.
  • Proficient in building and deploying models using PyTorch or TensorFlow in production-grade environments.
  • Solid programming skills in Python and C++
Responsibilities
  • Optimize deep learning models for low-latency, high-throughput inference.
  • Convert and deploy models using frameworks such as TensorRT and TensorRT-LLM
  • Understand, analyze, profile, and optimize performance of deep learning workloads on state-of-the-art hardware and software platforms.
  • Collaborate with internal and external researchers to ensure seamless integration of models from training to deployment.
Desired Qualifications
  • Proven experience deploying LLMs or VLMs at scale in real-world applications.
  • Hands-on experience with model optimization and serving frameworks, such as: TensorRT, TensorRT-LLM, vLLM, SGLang.

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 Rubin refreshes can drive another enterprise upgrade cycle.
  • Hyperscaler demand for AI infrastructure expands GPU, networking, and interconnect revenue.
  • Jetson, robotics, and edge AI broaden NVIDIA beyond data centers.

What critics are saying

  • Hyperscalers build custom accelerators, reducing dependence on NVIDIA GPUs.
  • China restrictions and domestic rivals like Huawei keep shrinking addressable demand.
  • Rubin execution slips open room for AMD and custom ASIC competitors.

What makes NVIDIA unique

  • CUDA and full-stack hardware-software integration create high switching costs.
  • Spectrum-X and NVLink Fusion extend control beyond GPUs into networking.
  • Annual architecture upgrades sustain first-mover advantage across AI, gaming, and robotics.

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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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