Research Intern 🎓

Hitachi Energy has an opening for the position of Research Intern in the field of AI transparency for Generative AI (GenAI) Systems. The hired intern will work under the supervision of Research Scientist(s) in the Hitachi Energy Research center. This position will be held in a hybrid-format or remotely within Canada.

Duration of internship: 4 months (can be extended up to 6 months as per need)

Start date: April 6th (flexible)

Mode of work: Remote or Hybrid (flexible)

Renumerated Internship

How you’ll make an impact

• Conduct an in-depth literature survey on AI interpretability, Explainable AI (XAI), and Human-AI Interaction, focusing on transparency in GenAI systems.

• Investigate methodologies and frameworks that interpret decisions from AI models applicable across diverse domains such as computing, coding, software engineering, and other technical fields.

• Implement ML/GenAI methodologies to develop solutions for human-centered transparency. Run experiments and prepare codebase.

• Prepare a technical report, presentation on work accomplished during internship and/or publication in peer-reviewed conference.

Your background

• PhD students/candidates in Computer Science/Engineering, Machine Learning, Software/Electrical Engineering. Excellent senior master’s (thesis-based) students with relevant experience are also welcome to apply.

• In-depth understanding of AI/ML methodologies required, including model architectures, training and model evaluation techniques.

• Experience with Python and ML libraries (e.g., Tensorflow/Pytorch, Scipy, SkLearn) is required.

• Familiar with GenAI implementation, prompting, evaluation, and agent/tool‑use patterns (e.g., retrieval‑augmented generation, LangChain, etc).

• Experience with implementing ML/GenAI solutions and applying quantitative and qualitative evaluation metrics.

• Research experience involving defining problems, exploring potential solutions, and analyzing results, with the ability to clearly present findings to key stakeholders.

• Good communication skills (both written and spoken).

Preferred qualification

If you have any of the following qualifications, please ensure to clearly mention them in your resume.

• Familiarity with XAI/ Interpretability libraries and methods (e.g., SHAP/ LIME, feature attribution, causal or counterfactual reasoning, familiarity with mechanistic interpretability literature).

• Experience with OpenAI/Azure/Open‑Source LLMs.

• One or more first-authored publications in top AI/ML conference/journals.

• Ability to do critical and innovative thinking. Ability to take lead in realizing ideas.

• Experience with multi-agent frameworks, and visualization techniques for interpretability.

• Experience working with large datasets.