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Pytorch Internship Jobs in Raleigh, NC (NOW HIRING)

Duke University

Quantitative Associate

Durham, NC · On-site

$125K - $140K/yr

Prior internship or project experience in finance, asset management, or a related quantitative ... Exposure to machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and techniques.

Duke University

Quantitative Associate

Durham, NC

$125K - $140K/yr

Prior internship or project experience in finance, asset management, or a related quantitative ... Exposure to machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and techniques.

All JobsPytorch Internship JobsPytorch Internship Jobs in Raleigh, NC

Pytorch Internship information

See Raleigh, NC salary details

$8

$16

$23

How much do pytorch internship jobs pay per hour?

As of Jun 18, 2026, the average hourly pay for pytorch internship in Raleigh, NC is $16.82, according to ZipRecruiter salary data. Most workers in this role earn between $14.04 and $18.70 per hour, depending on experience, location, and employer.

What types of projects and collaborative experiences can I expect during a PyTorch Internship?

During a PyTorch Internship, you can expect to work on hands-on machine learning and deep learning projects that involve developing, testing, and optimizing models using the PyTorch framework. Interns often collaborate closely with research scientists, software engineers, and product teams to contribute to real-world applications and open-source initiatives. You may participate in code reviews, brainstorming sessions, and weekly progress meetings, gaining exposure to both independent tasks and team-based problem-solving. This environment fosters both technical growth and communication skills, preparing you for advanced roles in AI and machine learning.

What are the key skills and qualifications needed to thrive as a PyTorch Intern, and why are they important?

To thrive as a PyTorch Intern, you need a solid background in Python programming, machine learning fundamentals, and familiarity with deep learning concepts, typically evidenced by coursework or project experience. Proficiency in PyTorch, version control systems like Git, and tools such as Jupyter Notebooks is highly valued. Strong problem-solving skills, attention to detail, and effective communication help interns contribute meaningfully to team projects and learn quickly. These skills and qualities are crucial for efficiently developing, testing, and deploying machine learning models in a collaborative environment.

What is the difference between Pytorch Internship vs Machine Learning Intern?

AspectPytorch InternshipMachine Learning Intern
Required SkillsProficiency in Pytorch, Python, deep learning conceptsPython, machine learning algorithms, data analysis
Work EnvironmentResearch labs, tech companies, AI startupsTech firms, research institutions, data-driven companies
Industry UsageDeep learning projects, neural network developmentBroader ML applications, data modeling

Both roles involve working with machine learning, but a Pytorch Internship specifically focuses on deep learning frameworks like Pytorch, while a Machine Learning Intern may work across various ML techniques. The Pytorch Internship is ideal for those specializing in neural networks and deep learning, whereas the Machine Learning Intern role covers a wider range of ML applications.

What is a PyTorch internship?

A PyTorch internship is a temporary position, often for students or recent graduates, where individuals gain hands-on experience working with the PyTorch deep learning framework. Interns typically assist with machine learning projects, develop and test models, and contribute to research or product development involving artificial intelligence. These internships provide valuable exposure to real-world applications of AI, opportunities to collaborate with experienced engineers and researchers, and a chance to enhance programming and problem-solving skills. Many internships also offer mentorship and may lead to full-time roles in the field.
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Pytorch Internship jobs near you
Infographic showing various Pytorch Internship job openings in Raleigh, NC as of June 2026, with employment types broken down into 15% As Needed, 69% Full Time, 4% Part Time, 8% Temporary, and 4% Contract. Highlights an 85% Physical, 1% Hybrid, and 14% Remote job distribution, with an average salary of $34,990 per year, or $16.8 per hour.
Agentic AI Engineer

Agentic AI Engineer

Cadence Design Systems, Inc.

Cary, NC • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Agentic AI Engineer
Role Summary
Cadence is hiring early-career Agent AI Engineers to join our applied AI team building agentic systems for silicon design. You will work alongside senior AI engineers and chip-design domain experts on the core technical pillars of Cadence's agentic stack: training and adapting models for engineering tasks, engineering high-quality design context (RAG, prompt scaffolds, retrieval pipelines), and tuning the knowledge graphs and vector/graph databases that ground our agents. From day one you will be writing production code that lands in customer-facing AI products and directly accelerates how the world designs chips.
What You Will Do
  • Model Development. Train, fine-tune, distill, and evaluate LLMs / SLMs and embedding models for EDA-specific tasks. Hands-on with LoRA / PEFT, instruction tuning, preference optimization (DPO/GRPO), and rigorous eval harnesses for code and reasoning.
  • Design Context Engineering. Build the retrieval pipelines, prompt scaffolds, and tool-calling specs that feed Cadence agents the right design context (RTL, scripts, logs, reports, methodology docs) at the right token budget. Optimize for accuracy, latency, and cost.
  • Knowledge Graph & Database Tuning. Design schemas, tune ingestion, and optimize queries for graph DBs (Neo4j, ArangoDB, NebulaGraph) and vector stores (Qdrant, Weaviate, pgvector, Chroma). Keep retrieval fast, accurate, and scoped to the right design hierarchy.
  • Agent Building Blocks. Implement and harden agent tools, memory, multi-hop reasoning patterns, and guardrails. Triage production failures and iterate.
  • Data Pipelines. Curate, clean, and label datasets from EDA artifacts (RTL, waveforms, logs, reports, schematics). Build synthetic-data and self-improvement loops where appropriate.
  • Evaluation & Telemetry. Build offline benchmarks and online metrics. Help define what 'good' looks like for chip-design agents and keep regressions out of main.
  • Collaborate & Learn. Pair with senior AI engineers, BU teams, and silicon domain experts. Learn the EDA flow as you go - we'll invest in you if you invest in the craft.

Must-Have Qualifications
  • BS / MS / PhD in CS, EE, ECE, AI/ML, or a closely related field (graduating in 2025-2026; recent grads also welcome).
  • Strong fundamentals in deep learning, transformers, and modern LLM mechanics (attention, tokenization, context windows, decoding).
  • Practical hands-on experience (coursework, internships, OSS, or serious side projects) with at least TWO of: LLM fine-tuning, RAG / retrieval, agentic frameworks, knowledge graphs, vector databases.
  • Solid Python engineering: comfortable with PyTorch and Hugging Face; writes clean, tested, version-controlled code.
  • Curiosity about silicon / chip design and willingness to learn a deep technical domain on the job.
  • Strong written and verbal communication; bias to ship working code over perfect plans.

Nice-to-Have / Bonus
  • Prior internship in AI/ML at a product company or research lab with shipped artifacts.
  • Hands-on with at least one agentic framework: LangGraph, AutoGen, Cursor SDK, Claude Code, MCP-based tool-calling stacks.
  • Experience with graph DBs (Neo4j, ArangoDB, NebulaGraph) and / or vector DBs (Qdrant, Weaviate, pgvector, Chroma, Milvus).
  • ML systems / infra exposure: vLLM, TGI, Triton, distributed training, GPU performance tuning, quantization.
  • Coursework or projects in compilers, formal methods, hardware description languages (Verilog/SystemVerilog/Chisel), or EDA tools.
  • Publications, OSS contributions, or competitive ML records (Kaggle medals, MLPerf, agent benchmarks, hackathon wins).

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