About the position
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.
Responsibilities
- 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.
Requirements
- Currently enrolled as a Junior or Senior undergraduate, or a Graduate (MS or MBA) student in Computer Science, Data Science, Electrical Engineering, Information Systems, or a related field.
- Demonstrated experience building AI applications — whether through coursework, personal projects, open-source contributions, or prior internships.
- Proficiency in Python with hands-on experience using ML libraries such as NumPy, Pandas, scikit-learn, PyTorch, or TensorFlow.
- Experience working with LLM/GenAI platforms (e.g., OpenAI API, Anthropic Claude, LangChain, RAG pipelines, or prompt engineering).
- Familiarity with cloud platforms (AWS, Azure, or GCP) and data tools including SQL and data pipeline or dashboard tooling.
- Strong written and verbal communication skills; able to present technical concepts clearly to both technical peers and non-technical stakeholders.
- Self-starter with the ability to move fast, iterate, and learn from ambiguous, real-world data problems.
Nice-to-haves
- 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.
Benefits