Role Overview
This internship is focused on ML research mixed with applied ML and software engineering. Topic of the internship is around trust and safety in LLMs such as:
• prompt injections
• safety alignment and guardrails
• privacy and confidentiality
• interpretability
Other topics closer aligned with the student's research may be considered.
Expected Background: ML
• Research experience • Ability to pose relevant questions, find answers in existing literature or plan and execute experiments.
• Ability to read and write research papers and technical articles.
• PhD student or student at any level with research experience and peer-reviewed published work.
• Machine learning tools: pytorch , huggingface , transformers , datasets.
• Applied deep learning and LLM experience. • Training and evaluating deep models.
• (nice to have) finetuning LLMs, multi-modal LLMs.
• (nice to have) Familiarity with ML [ NLP , LLM , Vision ] interpretability methods, sparse autoencoders , linear probes.
Expected Background: Software Engineering
• Development environments and tools: • unix , git , basic clouds usage on AWS and/or GCP
• jupyter
• Programming: • python
• (nice to have) “programming languages well-roundedness”
• experience in statically-typed and functional languages
Compensation & Benefits
• Market aligned compensation for interns in the bay area.
Requirements
• Must be authorized to work in the USA or must be able to obtain CPT (Curricular Practical Training) approval from host university.
• The internship is expected to last at least 3 months and intersect most of the period starting from January, 2027 through end of March 2027.