Goodfire
Research Scientist
San Francisco, CA
Sponsorship not specified$200k-$400kDetected 149 days ago
PythonMachine LearningResearchCommunication
About the role
- Goodfire is a research company using interpretability to understand, learn from, and design AI systems. Our mission is to build the next generation of safe and powerful AI—not by scaling alone, but by understanding the intelligence we're building.
- Scaling has proven powerful, but today's approach is fundamentally limited: we can't meaningfully understand, debug, or shape what models learn. Every engineering discipline has been gated by fundamental science and AI is at that inflection point now.
Responsibilities
- Collaborate with engineering to turn research into production-ready tools.
- Drive to move quickly and take ownership.
Requirements
- PhD or equivalent experience in ML, computer science, or a quantitative science.
- Required experience
Nice to have
- Experience leading research or contributing to open-source codebases.
- Familiarity with interpretability, alignment, or safe model development.
- Experience in startup or fast-paced lab environments.
- Put mission and team first
- We trust each other, deeply care about the success of the organization, and choose to put our team above ourselves.
- Improve constantly
- We proactively critique ourselves and others in a kind and thoughtful way that translates to practical improvements in the organization.
- Take ownership and initiative
Skills
- Interpretability mechanisms - Foundational research on how models represent and process information.
- Moonshots - High-upside bets on novel techniques that could unlock breakthroughs in model understanding.
- Applied research & usability - Translating research into tools for real-world users and enterprise applications.
Compensation
- The expected salary range for this position is $200,000 - $400,000 USD
Benefits
- This role offers market competitive salary, equity, and competitive benefits.
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This listing is sourced directly from Goodfire's careers page and normalized into a canonical job model.