Conduct ML research and applied work on LLM trust and safety (prompt injection, alignment, privacy, interpretability). Design and run experiments, read/write research papers, fine-tune and evaluate deep models, and implement research code using PyTorch/HuggingFace tools. Collaborate with engineers to productionize research; related student research topics may be considered.
Role Overview
Expected Background: ML
Expected Background: Software Engineering
Compensation & Benefits
Requirements
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.
- 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.
- 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
- Market aligned compensation for interns in the bay area.
- Must be authorized to work in the USA or must be able to obtain CPT (Curricular Practical Training) approval from host university.
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