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Internship Tokenization Jobs (NOW HIRING)

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... tokenization. • Experience processing and structuring raw data from Cameras, LiDAR, and Radar ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... tokenization. • Experience processing and structuring raw data from Cameras, LiDAR, and Radar ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... tokenization. • Experience processing and structuring raw data from Cameras, LiDAR, and Radar ...

UMB Financial

IT Blockchain Intern

Kansas City, MO · On-site +1

$25K - $49K/mo

Conduct research and develop on blockchain platforms, digital assets, tokenization, smart contracts ... Relevant coursework, internship or project experience related to blockchain, fintech or digital ...

D3

Frontend Engineer - New Grad

Los Angeles, CA

... tokenization and DeFi primitives to a massive, rapidly growing $350B+ real-world asset class . We ... Previous coursework, projects, or internships involving blockchain, NFTs, or dApps. Why D3, Why Now?

D3

Frontend Engineer - New Grad

Los Angeles, CA · On-site

... tokenization and DeFi primitives to a massive, rapidly growing $350B+ real-world asset class . We ... Previous coursework, projects, or internships involving blockchain, NFTs, or dApps. Why D3, Why Now?

D3

Frontend Engineer - New Grad

Los Angeles, CA · On-site

... tokenization and DeFi primitives to a massive, rapidly growing $350B+ real-world asset class . We ... Previous coursework, projects, or internships involving blockchain, NFTs, or dApps. Why D3, Why Now?

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Internship Tokenization information

What kinds of projects do interns typically work on during a tokenization internship?

Interns in tokenization roles often participate in projects related to developing or refining digital asset platforms, designing smart contracts, and supporting the creation of tokens for real-world assets. Daily responsibilities may include researching token standards, assisting in compliance checks, performing market analyses, and collaborating closely with blockchain developers and legal teams. This hands-on experience provides interns with exposure to both the technical and regulatory aspects of tokenization, as well as the opportunity to contribute to innovative solutions in the growing digital asset sector.

What is the difference between Internship Tokenization vs Data Analyst?

AspectInternship TokenizationData Analyst
Required CredentialsTypically pursuing or recent graduate, some technical skillsBachelor's or higher in data-related fields, certifications preferred
Work EnvironmentInternship setting, entry-level tasks, learning-focusedFull-time, office or remote, analytical and reporting tasks
Employer & Industry UsageTech, finance, startups, companies experimenting with blockchainFinance, marketing, healthcare, tech industries

Internship Tokenization involves entry-level roles focused on blockchain and digital assets, often for students or recent graduates. Data Analysts analyze data to inform business decisions. While both roles require analytical skills, Internship Tokenization emphasizes blockchain knowledge, whereas Data Analysts focus on data management and interpretation.

What is an Internship in Tokenization?

An Internship in Tokenization typically involves working with technologies that convert real-world assets or data into digital tokens on a blockchain or similar platforms. Interns may assist with research, development, and testing of tokenization solutions, which can include cryptocurrencies, NFTs, or asset-backed tokens. The role is ideal for those interested in blockchain technology, fintech, and digital asset management. Interns gain hands-on experience with smart contracts, regulatory compliance, and emerging trends in digital finance.

What are the key skills and qualifications needed to thrive as an Internship Tokenization specialist, and why are they important?

To thrive in an Internship Tokenization role, you need a solid understanding of blockchain fundamentals, digital assets, and related financial concepts, typically supported by coursework or experience in computer science, finance, or a related field. Familiarity with smart contract platforms (like Ethereum), tokenization platforms, and tools such as Solidity or Python is often required. Strong analytical thinking, attention to detail, and effective communication skills help you navigate complex projects and collaborate with cross-functional teams. These skills are essential to ensure secure, compliant, and innovative tokenization solutions in a rapidly evolving digital finance landscape.
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Internship Tokenization jobs near you
AI Engineer (Internship) - Intelligent Question Bank Platform

AI Engineer (Internship) - Intelligent Question Bank Platform

Accel Learning

Secaucus, NJ

Internship

Posted 8 days ago

Job description

Company Description

Accel Learning is a New Jersey-based tutoring and test-prep center serving 3,000+ students across K–12 and beyond. We prepare students for some of the most competitive exams in the country - SAT, ACT, ISEE, BCA, SSAT, HSPT, GRE, GMAT, Regents, NJSLA, OLSAT, SCAT, TerraNova, Praxis, PSEG, Math Olympiad, AMC, MathCounts, and more.
Our Mission: Make rigorous, personalized learning accessible. Today our instructors build question banks manually in WordPress. We're changing that by building an AI-powered question generation engine that creates exam-ready, curriculum-aligned questions at scale.

Job Description

You will architect and implement the core AI pipeline that powers Accel's test creation system.

• Work closely with the founder to design and build an AI-powered content generation system from the ground up. You'll contribute to meaningful parts of the product end-to-end from how the system ingests and understands source material, to how it produces and validates outputs, to how instructors interact with and review what the system generates.
• On the engineering side, you'll build and iterate on LLM-driven pipelines, work with retrieval and embedding techniques to ground outputs in real source material and develop backend services and APIs that tie everything together.
• Beyond pure coding, you'll be expected to think about output quality and building evaluation steps, catching failure modes, and improving the system based on real instructor feedback. You'll research new tools and techniques as the AI space evolves and bring relevant ideas directly into the product.
• This is a generalist role at an early-stage product where you'll wear multiple hats, work with ambiguity, and have direct input into how things are built.

PLEASE NOTE THESE QUESTIONS AND REPLY WITH YOUR APPLICATION:

  1. This is an unpaid internship opportunity. Are you still interested in the role?
  2. What interests you most about this internship and this role? (Please share what excites you about contributing and what you hope to gain from the experience.)
  3. Tell us about the most interesting project you’ve worked on in this domain. What was the project, and what specific contributions did you make? (Include technologies, responsibilities, outcomes, or measurable impact if applicable.)
  4. How many hours per week are you available to commit to this internship?
  5. Are you currently based in the USA?
Qualifications

• Strong foundation in software engineering: data structures, APIs, system design
• Proficiency in Python (primary language for AI/ML pipeline work)
• Experience with REST APIs and at least one database (PostgreSQL preferred)
• Ability to work independently, ask sharp questions, and iterate fast
• Strong debugging and problem-solving instincts
• Demonstrated side projects or shipped code (GitHub portfolio required)
• Genuine interest in AI systems and education technology

• Direct experience with LLM APIs: OpenAI, Anthropic Claude, or Google Gemini
• Hands-on experience with RAG systems: embedding models, vector databases (Pinecone, 
Weaviate, pgvector, Chroma)
• Familiarity with prompt engineering techniques: few-shot prompting, chain-of-thought, 
structured JSON outputs
• Experience with NLP pipelines: text chunking, tokenization, semantic search
• Knowledge of LaTeX syntax and math rendering libraries (MathJax, KaTeX)
• Experience with image generation APIs or SVG programmatic generation
• Familiarity with AI evaluation frameworks or automated test harnesses for LLM outputs
• Cloud platform experience: AWS, GCP, or Vercel for deployment
• Experience with job queues: Celery, Bull, or similar
• Exposure to educational content standards or psychometrics is a bonus


Additional Information

All your information will be kept confidential according to EEO guidelines.