Research Intern, Efficient Deep Learning

NVIDIA

. Research, design, and implement novel methods for efficient deep learning in diffusion LLMs and multimodal models .

Posted 10/6/2026full-timeRemote • California • United StatesEntry Level💰 $38 - $94 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deep learning, particularly with large language models and multimodal systems, while possessing a strong research background and the ability to collaborate effectively with teams and external researchers.

Highest-signal resume keywords

Ph.D. In Computer Science/EngineeringMachine Learning And Deep LearningLarge Language ModelsModel Training And ParallelizationParallel Programming (CUDA)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningLarge Language ModelsMultimodal ModelsModel TrainingData PreparationModel ParallelizationPruningQuantizationNeural Architecture Search (NAS)Efficient Backbones

Soft Skills

Excellent Communication Skills

Tools & Technologies

CUDAHybrid Cloud–Edge InferenceOrchestrationAdaptive Routing

Industry Keywords

Diffusion Language ModelsAgentic SystemsSampling EfficiencyAdaptive UnmaskingSelf-SpeculationTraining PipelinesDistillation PipelinesRouting PoliciesScheduling PoliciesResource-Aware Agent Loops

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Research, design, and implement novel methods for efficient deep learning in diffusion LLMs and multimodal models
Research efficient agentic AI with hybrid inference orchestration across cloud and edge
Work on sampling efficiency, adaptive unmasking, self-speculation / parallel decoding, training and distillation pipelines, and multimodal generation
Develop routing and scheduling policies, on-device versus cloud expert delegation, and resource-aware agent loops
Publish original research
Collaborate with team members and teams
Work with product groups to transfer technology
Collaborate with external researchers

Requirements

What you’ll need

Pursuing a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc.
Excellent knowledge of theory and practice of machine learning and deep learning
Experience with large language models, diffusion language models, multimodal / vision-language models, or agentic systems is required
Hands-on experience with large-scale model training including data preparation and model parallelization (tensor and pipeline) is required
Outstanding research track record with at least one top-tier conference (ICML, ICLR, NeurIPS, CVPR, ICCV, etc.)
Excellent communication skills
Parallel programming (e.g., CUDA)
Interest or experience in hybrid cloud–edge inference, orchestration, or adaptive routing
Background in pruning, quantization, NAS, or efficient backbones

Benefits

Comp & perks

Competitive salaries
Generous benefits package
Intern benefits