AI Inference & Training Framework Engineer Intern

Santa Clara, CAOnsiteInternshipJunior / Entry-levelPosted 2 hours ago

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Required skills

AI models
model serving
distributed training
scheduling
caching
framework integration
runtime performance
ML systems
distributed systems
AI infrastructure

Nice to have

TensorFlow
SGLang
multiple GPUs
CUDA
GPU programming
Docker

Role summary

NeuroSpark is seeking an AI Inference & Training Framework Engineer Intern to work on systems for running, scaling, and optimizing AI models. Projects will focus on areas like model serving, distributed training, scheduling, caching, communication, framework integration, and runtime performance. This role is ideal for students interested in ML systems, distributed systems, or AI infrastructure, with no prior full-time industry experience required. Interns will develop and benchmark framework components, integrate models using frameworks like PyTorch or JAX, analyze performance metrics, experiment with execution strategies, build multi-GPU prototypes, develop tooling, and debug AI workloads.

About the Role

As an AI Inference & Training Framework Intern at NeuroSpark, you will work on systems that run, scale, and optimize modern AI models. Projects may involve model serving, distributed training, scheduling, caching, communication, framework integration, or runtime performance. This internship is a strong fit for students interested in ML systems, distributed systems, or AI infrastructure. Prior full-time industry experience is not required.

What You’ll Do

Develop and benchmark inference or training framework components.
Run and integrate models using PyTorch, JAX, vLLM, or related frameworks.
Analyze latency, throughput, memory usage, and distributed performance.
Experiment with batching, caching, scheduling, and parallel execution.
Build multi-GPU inference or training prototypes.
Develop testing, profiling, and developer tooling.
Debug performance and correctness issues in real AI workloads.
Work directly with engineers on production-relevant infrastructure problems.

Minimum Qualifications

Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or a related field.
Strong programming ability in Python, C++, or both.
Coursework or project experience in distributed systems, operating systems, machine learning systems, networking, or parallel computing.
Familiarity with at least one modern ML framework.
Comfortable developing in a Linux environment.
Strong interest in understanding how large-scale AI systems work under the hood.

Preferred Qualifications

PyTorch, JAX, TensorFlow, vLLM, or SGLang experience.
Experience running models across multiple GPUs.
CUDA or GPU programming exposure.
Familiarity with distributed training or inference.
Experience with Docker, Kubernetes, cloud infrastructure, or networking.
Research, personal projects, or open-source contributions related to ML systems.

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