Division: Supply Chain Intelligence
Level: Undergraduate (Junior/Senior) or Graduate (MS/MBA)
Location: Denver, CO - Hybrid (3 days on-site per week)
Duration: June 1 - August 3-10, 2026 | 9-10 weeks
Compensation: Paid - $17/hour
ABOUT THE ROLE
Accuris's Supply Chain Intelligence division is transforming how engineers, procurement teams, and sustainability leaders understand the global electronics supply chain. We are looking for a creative, technically strong AI/ML Summer Intern to join our team and help build the next generation of AI-powered capabilities - from carbon footprint calculators for electronic components to predictive algorithms for supply chain risk and availability.
This is a hands-on, build-first internship. You will go from idea to working prototype, collaborating closely with product managers, engineers, and data scientists. By the end of the summer, you will have shipped a real AI tool and presented it to audiences ranging from engineers to senior executives.
WHAT YOU'LL WORK ON
- Design and build AI-powered prototypes such as carbon footprint calculators for electronic components or predictive models for supply chain risk, demand, and component availability.
- Apply LLM and generative AI techniques to create intelligent, data-driven tools using platforms like OpenAI, Anthropic Claude, or LangChain.
- Develop and validate machine learning models using Python and standard ML libraries (scikit-learn, PyTorch, TensorFlow, etc.).
- Work with cloud-based data pipelines, SQL databases, and dashboards to source and transform supply chain data.
- Use rapid "vibe coding" methodologies to iterate quickly on AI concepts and validate ideas early.
- Translate your technical work into clear, compelling presentations for both engineering teams and executive audiences.
WHAT YOU'LL DELIVER
By the end of the summer, you will be expected to deliver two things:
- A working AI prototype - a functional tool or model that demonstrates clear value against a supply chain intelligence use case (e.g., component carbon footprint estimator, predictive availability scorer, or similar).
- An executive-ready presentation - a polished deck communicating your approach, methodology, findings, and recommended next steps for the business.
REQUIRED QUALIFICATIONS
PREFERRED QUALIFICATIONS
- Prior exposure to supply chain, electronics manufacturing, procurement, or sustainability/ESG domains.
- Familiarity with carbon accounting frameworks, life cycle assessment (LCA), or sustainability data (e.g., GHG Protocol, Scope 3 emissions).
- Experience building and evaluating predictive models for time-series, classification, or regression problems.
- Active portfolio of AI/ML projects (e.g., GitHub, Kaggle, Hugging Face, or personal website).
- Comfort with rapid prototyping and "vibe coding" - the ability to quickly scaffold and iterate on AI-driven tools.
ABOUT ACCURIS
Accuris provides engineers, procurement specialists, and product teams with trusted data and intelligence to design better products and build more resilient supply chains. Our Supply Chain Intelligence division delivers real-time component data, risk analytics, and predictive insights to help global organizations make faster, smarter sourcing decisions. This internship puts you at the frontier of AI applied to one of the world's most complex and consequential industries.
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What We Do
Accuris empowers the next generation of engineers to solve tomorrow’s problems today. Our combination of trusted data, insights, and innovative technology solutions enable business and technical leaders to transform their workflows and make decisions with conviction - bringing their dreams to life in faster, smarter, and cleaner ways.
Why Work With Us
We’re a global team that strives to think differently – combining the knowledge and resources of an established company with the bold nature of a startup. If you’re eager to make a lasting impact, to discover and bring to life new value and opportunities, and have a bias toward action and learning, you’re in the right place.
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Accuris Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.


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