Research Engineer

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
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