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Internship Ai Coding information

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$13

$24

$37

How much do internship ai coding jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for internship ai coding in Raleigh, NC is $24.71, according to ZipRecruiter salary data. Most workers in this role earn between $20.10 and $28.03 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI Coding Intern, and why are they important?

To thrive as an AI Coding Intern, you need a solid understanding of programming languages like Python, foundational knowledge in algorithms, data structures, and basic machine learning concepts, often supported by coursework or personal projects. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems like Git is typically expected. Strong problem-solving skills, eagerness to learn, and effective communication make an intern stand out. These skills and qualities are crucial for successfully contributing to AI projects, learning from mentors, and adapting to the fast-paced field of artificial intelligence.

What is the difference between Internship Ai Coding vs Data Analyst Intern?

AspectInternship Ai CodingData Analyst Intern
Required SkillsProgramming, AI/ML concepts, Python, TensorFlowData analysis, Excel, SQL, statistical skills
Work EnvironmentTech companies, AI startups, R&D teamsBusiness, finance, marketing departments
Industry UsageArtificial Intelligence, Machine Learning, Software DevelopmentBusiness Intelligence, Data Management, Reporting

Internship Ai Coding focuses on developing AI models and programming skills in a tech environment, often involving machine learning projects. Data Analyst Internships emphasize analyzing data sets, creating reports, and supporting business decisions. Both roles require technical skills but differ in their focus and industry applications.

What is an AI coding internship?

An AI coding internship is a temporary position where students or recent graduates work with artificial intelligence technologies, focusing on programming and software development tasks. Interns typically assist in designing, coding, and testing AI models or applications under the guidance of experienced professionals. These internships provide hands-on experience with machine learning, data analysis, and software engineering, often using languages like Python or frameworks such as TensorFlow and PyTorch. The goal is to help interns gain practical skills and industry knowledge that can support a future career in AI or related fields.

What types of projects can I expect to work on during an AI Coding internship?

As an AI Coding intern, you can typically expect to contribute to projects such as developing machine learning models, automating data processing tasks, and supporting the integration of AI features into existing applications. Interns often collaborate with data scientists and software engineers, gaining hands-on experience with real datasets and industry-standard tools. Daily responsibilities might include coding, testing algorithms, participating in code reviews, and presenting project updates to the team. This role offers a valuable opportunity to build technical skills while working in a collaborative, mentorship-driven environment.
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What cities near Raleigh, NC are hiring for Internship Ai Coding jobs? Cities near Raleigh, NC with the most Internship Ai Coding job openings:
Internship Ai Coding jobs near you
Agentic AI Engineer

Agentic AI Engineer

Cadence Design Systems, Inc.

Cary, NC • On-site

Full-time

Posted 7 days ago

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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