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Posted 9 days ago
Job description
Research Engineer, Interpretability Systems
📍 San Francisco Bay Area (On-site)
I am partnering with an early-stage AI research lab founded by former frontier-model researchers, focused on alignment and interpretability for large language models. They are building experimental systems and tooling designed to better understand how advanced models reason internally, moving beyond black-box behavior toward mechanistic understanding and controllability.
I’m looking for Research Engineers who want to build the experimental infrastructure that makes cutting-edge interpretability research possible.
What You’ll Do:
- Build custom RL-style environments and experimental testbeds for interpretability research
- Develop tooling to probe internal representations, including activation tracing, concept detection, and mechanistic analysis
- Implement probes that detect latent concepts such as deception, uncertainty, goals, or hidden objectives
- Prototype activation-level steering methods that go beyond prompting or fine-tuning
- Work closely with researchers to rapidly iterate from idea → implementation → experiment → result
- Help define new benchmarks and measurement frameworks for understanding internal model consistency and robustness
- Build tooling that enables entirely new classes of alignment and interpretability experiments
Required:
- Strong software engineering fundamentals and experience building experimental ML systems
- Experience working close to model internals, representations, or post-training systems
- Strong Python and deep learning framework experience (PyTorch preferred)
- Ability to rapidly prototype and iterate in open-ended research environments
- Experience in interpretability, alignment, or ML research preferred
- PhD ideal
Apply today or contact Ethan - elewis@acceler8talent.com