Full-Time
Head of People
Goodfire
51-200 employees
AI interpretability tools and safety infrastructure
Compensation Overview
$400k - $500k/yr
San Francisco, CA, USA
In Person
On-site role in San Francisco, CA; five days a week.
 People & HR (1) |
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- 8+ years in People/Talent roles, including experience building or scaling a People function at a high-growth startup
- Track record of hiring for technical/research roles under intense pressure without compromising quality
- Comfortable operating with ambiguity and building processes from scratch
- Strong judgment on sensitive, high-stakes people situations
- Own Goodfire’s hiring strategy and execution across all functions
- Own compensation, leveling, and equity philosophy to ensure we attract and retain world-class talent
- Own the end-to-end employee experience, from offer through onboarding, development, performance, and offboarding
- Shape and reinforce a high-performance, mission-driven culture as the company scales while preserving trust, intensity, and high standards
- Build scalable People systems, processes, and infrastructure that enable rapid growth
- Partner closely with founders and leaders on organizational design, workforce planning, succession planning, and leadership development
- Lead employee relations and navigate sensitive, high-stakes people situations with sound judgment, discretion, and empathy
- Hire, develop, and lead a high-performing People team
Goodfire builds infrastructure and developer tools that allow users to understand, edit, and debug artificial intelligence models. These tools work by providing a practical interface for inspecting the internal logic of AI, enabling developers to identify and fix errors within complex systems at scale. Unlike many competitors that focus solely on theoretical research, Goodfire operates as a public benefit corporation that bridges the gap between science and practical application through specialized debugging software. The company's goal is to ensure the creation of safer and more reliable AI by making model behavior transparent and manageable for researchers and organizations.
Company Size
51-200
Company Stage
Series B
Total Funding
$207M
Headquarters
San Francisco, California
Founded
2024
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See people who can refer or advise you
Simplify's Take
What believers are saying
- Goodfire raised $150 million in February 2026 at a $1.25 billion valuation.
- Mayo Clinic and Goodfire linked interpretability to Alzheimer’s biomarkers in April 2026.
- Silico and Ember monetize growing demand for controllable, debuggable foundation models.
What critics are saying
- OpenAI, Anthropic, and Google DeepMind can internalize interpretability features by 2027.
- Silico's case-by-case pricing limits repeatable sales, slowing enterprise scaling against broader platforms.
- If mechanistic interpretability stalls scientifically, Goodfire becomes an expensive research lab without durable product-market fit.
What makes Goodfire unique
- Goodfire's Silico exposes neurons and pathways, turning model debugging into software-like workflows.
- Anthropic backed Goodfire early, validating its mechanistic interpretability moat versus generic AI tooling.
- Customers like Microsoft, Mayo Clinic, and Arc Institute prove cross-market relevance.
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Benefits
Company Equity
Growth & Insights and Company News
6 month growth
↓ -4%1 year growth
↑ 3%2 year growth
↓ -7%San Francisco-based startup Goodfire has released Silico, a new tool that allows researchers to examine AI models and adjust their parameters during training. The tool uses mechanistic interpretability to map neurons and pathways inside models, enabling developers to reduce unwanted behaviours or steer outputs. Silico aims to transform AI model building from an opaque process into a more scientific, controllable one. By exposing internal mechanisms, Goodfire hopes to bring AI training closer to traditional software engineering practices, giving users unprecedented control over model development. The tool represents a significant step towards making large language models more transparent and debuggable, addressing longstanding concerns about the black-box nature of AI systems.
Goodfire has launched Silico, a mechanistic interpretability tool that allows developers to examine and adjust the inner workings of large language models. The tool enables users to zoom in on individual neurons or neuron groups within open-source models, run experiments to understand their function, and trace pathways to see how neurons interact. Using Silico, developers can modify parameters connected to specific neurons to boost or suppress certain behaviours. For instance, Goodfire researchers demonstrated how boosting neurons associated with transparency could change a model's ethical reasoning on disclosure decisions. The tool can also help filter training data to avoid setting unwanted parameter values. Goodfire aims to democratise techniques previously available only to top AI labs, making them accessible to smaller firms and research teams. Silico is available for a fee determined case-by-case.
Goodfire launches Silico: A game-changer for LLM debugging. Goodfire, a San Francisco-based startup, has unveiled Silico, a groundbreaking tool designed to enhance mechanistic interpretability in AI models. This innovative platform allows researchers and engineers to delve into the inner workings of large language models (LLMs), adjusting the parameters that define their behavior during training. According to Goodfire, Silico represents the first commercially available solution that facilitates debugging at every stage of AI development, from dataset creation to model training. CEO Eric Ho emphasizes the company's mission to transform AI model development from a mysterious process into a scientific discipline, addressing the existing knowledge gap between model deployment and understanding. Mechanistic interpretability is a cutting-edge approach that seeks to unveil the complexities of AI operations by mapping neural pathways and their interactions. This technique is gaining traction among industry leaders like Anthropic, OpenAI, and Google DeepMind, and has been recognized by MIT Technology Review as one of its Breakthrough Technologies. Goodfire aims not only to audit existing models but also to streamline the design process, eliminating the trial-and-error nature of model training. With Silico, developers can fine-tune LLM behaviors, such as reducing instances of hallucination, by exposing and manipulating the model's parameters. The tool employs automated agents to handle much of the interpretative work, making it accessible for users without extensive expertise. While Silico offers promising capabilities, experts like Leonard Bereska from the University of Amsterdam urge caution. He acknowledges the tool's utility but warns that the term 'engineering' might overstate its precision, suggesting it primarily enhances the existing alchemical nature of AI model training. Silico enables users to examine individual neurons within a trained model, allowing for targeted experiments and deeper understanding of how specific inputs affect outputs. For instance, Goodfire identified a neuron linked to ethical dilemmas within an open-source model, demonstrating how modifications can shift a model's responses. Furthermore, Silico can assist in steering the training process by filtering out undesirable influences from training data, ultimately helping to create more reliable AI systems. By democratizing access to advanced interpretability techniques, Goodfire aims to empower smaller firms and research teams to develop tailored models that meet their unique needs.
Goodfire AI, a US-based AI research lab, has raised $150 million in Series B funding at a $1.25 billion valuation. The company was founded in 2023 by Eric Ho, Dan Balsam and Tom McGrath. Goodfire describes itself as a research company using interpretability to understand, learn from and design AI systems. The startup's mission is to build the next generation of safe and powerful AI through understanding rather than scaling alone. The company focuses on making AI systems more transparent and controllable by examining how they function internally.
/PRNewswire/ -- Today, Goodfire—the AI research lab using interpretability to understand, learn from, and design models—announced a $150 million Series B...