Goodfire is an AI interpretability research lab focused on understanding and intentionally designing advanced AI systems. Backed by Lightspeed, AI Grant and Menlo.
About the role
We’re looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You’ll play a central role in building our core technology, from training and eval tooling to product features, to achieve our mission of understanding and intentionally designing AIs.
What they're looking for
- 5+ years of experience in ML infra, research engineering, or systems programming
- Comfort working across research and engineering boundaries
- Expertise in Python, PyTorch or Jax, and distributed systems
- Experience deploying and maintaining ML systems at scale
- You care about understanding how models work internally and using that to make them more reliable and useful in the real world
More about this role
Goodfire is an AI interpretability research lab. We build interpretability agents and infrastructure to understand, monitor, and align AI models.
We believe that AI is the most consequential technology of our time. Yet AI models are trained by trial and error today, with limited understanding of what drives their behavior. Our goal is to advance the research and technology needed to fully understand and align AI so humanity can trust superintelligence.
Goodfire is a public benefit corporation founded by researchers who helped pioneer the field of interpretability at OpenAI and Google DeepMind. We are headquartered in San Francisco and have raised over $200M from leading investors including Lightspeed, Menlo, and B Capital.
We’re looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You’ll play a central role in building our core technology, from training and eval tooling to product features, to achieve our mission of understanding and intentionally designing AIs.
- Interpretability tools – Building the tools and infrastructure to support understanding and intentional design of models at industry...
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