Machine Learning Research Intern (Winter/Spring 2027)

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.