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• Analyze Systems Engineering Processes: Collaborate with systems engineers to identify manual, time-consuming technical processes (e.g., Requirements Analysis and Management, Verification and Validation (V&V), Risk Management, Knowledge Management etc) that can be improved with AI solutions.
• Develop AI tool Prototypes: Design and develop software prototypes and AI models (e.g., using Natural Language Processing (NLP) and Large Language Models (LLMs)) to automate or assist with systems engineering tasks.
• Implement Tools: Write clean, efficient, and testable code (primarily in Python) to integrate AI components into existing systems engineering tools and platforms, ensuring compliance with security and governance standards.
• Data Analysis & Model Optimization: Participate in the data collection, cleaning, and pre-processing stages, and assist in fine-tuning AI models for specific domain-related tasks.
• Testing & Validation: Assist in testing and evaluating developed AI tools to ensure they meet performance metrics and functional requirements.
• Documentation & Reporting: Maintain detailed documentation of AI model development, system architecture, and operational procedures, and prepare reports summarizing research outcomes.
• Innovation: Continuously explore emerging AI technologies and research, and recommend enhancements to standard processes.

• You'll analyze our performance results (sell-in/sell-out) and create reports and visualize the performance.
• You'll support and prepare product presentations to cross functional partners.
• You'll assess our benchmark competitors.
• You'll learn how to use PLM & other data systems to provide data support.
• You'll help the sell-in session organization and provide support.
• You'll manage the samples - sample organization/unpacking - sample/sale organization when needed.

Hitachi Energy Research, in close collaboration with business areas, is developing foundations for the next generation of Power Grids products. We focus on the challenges and opportunities in enhancing grid flexibility and facilitating renewable energy transition. In this context, we offer a 6 months internship position which provides the opportunity of working on real-world projects in an international team of research scientists. 

How you'll make an impact

• Develop multi-physics models for simulating sensors
• Verify and validate the models with experimental data 
• Collaborate with experts in a multi-disciplinary research team
• Present progress updates and results, and write technical reports
• Contribute to scientific publications.

• Support the global Procurement Innovation function in strengthening our end-to-end supplier-enabled innovation processes
• Create an onboarding handbook to help new team members quickly understand the processes and resources
• Develop a supplier innovation ranking system using existing data and introducing new ideas to evaluate innovation potential
• Support strategic planning by gathering focus areas and conducting deep dives with PD colleagues to inform future roadmapping cycles
• Provide day-to-day support for the team, contributing to ongoing projects and operational tasks as needed.

Roche has accumulated a vast collection of curated preclinical and clinical study reports spanning the full drug development lifecycle. Current commercial LLMs still underperform in specialized biomedical and pharmaceutical contexts. This internship focuses on advancing Roche’s internal LLM post-training capabilities to build domain-specialized LLMs for clinical study use by applying Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Human Feedback (RLHF). You’ll work side-by-side with Roche ML scientists to fine-tune open-source models, optimize them for pharmacological, translational-science and drug-discovery tasks, and help build the infrastructure and workflows needed to support model deployment in a drug-discovery context.

You’ll have access to high-performance GPU infrastructure (A100 clusters) and unique domain-specific data. You’ll start with smaller-sized LLMs to build the methodology, then iterate toward larger models and production-scale workflows.

• Data Creation: Approximately 10% of this intern’s work will be data collection and dataset creation.
• Model Post-Training: Fine-tune and align open-source LLMs (e.g., Llama, Mistral, Qwen) using Roche’s curated clinical and preclinical datasets through SFT, DPO, or RLHF.
• Pipeline Development: Implement and iterate on training pipelines using frameworks such as (including but not limited to) Hugging Face Transformers, TRL, NV Megatron-LM, or HF Smol.
• Evaluation: Design evaluation protocols for factual accuracy, safety, and alignment with biomedical domain knowledge.
• Experimentation: Start with small-scale LLMs to establish scalable training and evaluation workflows, progressing toward larger foundation models.
• Documentation: Maintain experiment logs, model cards, and reproducible training setups for internal knowledge transfer.