Research Engineer/Research Scientist – Model Transparency
Tasks
- Apply interpretability methods for model transparency
- Automate ML research pipelines
- Build AI agent orchestration systems
- Build white box and black box analysis tooling
- Conduct alignment evaluations
- Create and scale LLM evaluation workflows
- Design and run machine learning experiments
- Develop monitoring and auditing methods
- Review frontier lab risk assessments and safety cases
- Translate research findings into technical reports
- Write research code for ML experiments
Perks/Benefits
- N/A
Skills/Tech-stack
Activation analysis | Agent Orchestration | Alignment Evaluation | Black box testing | Black-box | Box testing | DPO | Distributed Computing | Evaluation Frameworks | Fine Tuning | Inoculation Prompting | Language Models | Large Language Models | Machine Learning | Mechanistic Interpretability | Model Interpretability | Probing | PyTorch | Python | RL Pipelines | RLHF | Red Teaming | Sparse Autoencoders | White-box | White-box testing
Education
N/A
Roles
Engineer | Research Engineer | Research Scientist | Scientist
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