Senior DL Algorithms Engineer - Cosmos at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

Requirements

Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience)
3+ years of professional experience in deep learning, applied machine learning, or physical AI development
Strong foundation in deep learning algorithms, including hands-on experience with LLMs, VLMs, and multimodal generative models such as World Foundation Models
Deep understanding of transformer architectures, attention mechanisms, and inference bottlenecks
Proficient in building, optimizing, and deploying models using PyTorch or TensorFlow in production-grade environments
Solid programming skills in Python and C++
Experience with model quantization and modern inference optimization techniques (e.g., KV cache, in-flight batching, parallelization mapping)
Strong fundamentals in GPU performance analysis and profiling tools (e.g., Nsight, nsys profiling)
Familiarity with serving models using Triton Inference Server and PyTriton via Docker

Responsibilities

Optimize deep learning models for low-latency, high-throughput inference, with a focus on LLMs, VLMs, diffusion models, and World Foundation Models (WFMs) designed for physical AI applications
Convert, deploy, and optimize models for efficient inference using frameworks such as TensorRT, TensorRT-LLM, vLLM, and SGLang
Understand, analyze, profile, and optimize performance of deep learning and physical AI workloads on state-of-the-art NVIDIA GPU hardware and software platforms
Implement and refine components and algorithms for efficient serving of LLMs, VLMs, and WFMs at datacenter scale, leveraging technologies like Dynamo
Collaborate with research scientists, software engineers, and hardware specialists to ensure seamless integration of cutting-edge AI models from training to deployment
Contribute to the development of automation and tooling for NVIDIA Inference Microservices (NIMs) and inference optimization, including creating automated benchmarks to track performance regressions

Skills

Key technologies and capabilities for this role

Deep LearningLLMsVLMsWFMsTensorRTTensorRT-LLMvLLMSGLangNVIDIA GPUDiffusion ModelsPhysical AIInference Optimization

Questions & Answers

Common questions about this position

What education and experience are required for this Senior DL Algorithms Engineer role?

A Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience) is required, along with 3+ years of professional experience in deep learning, applied machine learning, or physical AI development.

What technical skills are essential for this position?

Candidates need a strong foundation in deep learning algorithms including LLMs, VLMs, and multimodal generative models, proficiency in PyTorch or TensorFlow, solid programming skills in Python and C++, experience with model quantization and inference optimization techniques, and GPU performance analysis using tools like Nsight and nsys.

What frameworks and tools should I be familiar with for model deployment?

Experience with TensorRT, TensorRT-LLM, vLLM, SGLang, Triton Inference Server, and PyTriton via Docker is required, along with familiarity in serving models using these frameworks.

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

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What collaboration is involved in this role?

You will collaborate with research scientists, software engineers, and hardware specialists to bring cutting-edge AI models from prototype to production and ensure seamless integration.

What makes a candidate stand out for this position?

Proven experience deploying LLMs, VLMs, diffusion models, or World Foundation Models at scale in real-world applications, especially for robotics or autonomous vehicles, and hands-on experience with model optimization frameworks like TensorRT stand out.

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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