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 - View email address on click.appcast.io