Full-Time

Director of People

Posted on 9/9/2026

Goodfire

Goodfire

51-200 employees

AI interpretability tools and safety infrastructure

Compensation Overview

$250k - $400k/yr

+ Equity

San Francisco, CA, USA

In Person

On-site in the San Francisco office five days per week.

Category
People & HR (1)
Required Skills
Risk Management

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Responsibilities
  • Own and execute Goodfire’s People strategy across hiring, talent, compensation, employee experience, and organizational development as the company scales.
  • Own compensation, leveling, and equity philosophy, ensuring the approach is competitive, internally consistent, and aligned with goals to attract and retain exceptional talent.
  • Own the full employee lifecycle, from onboarding through offboarding, ensuring a smooth, high-quality employee experience at every stage.
  • Build scalable People systems, processes, and infrastructure that support rapid growth while maintaining a high bar for employee experience and operational excellence.
  • Lead benefits administration and strategy, including enrollments, changes, employee support, and vendor management.
  • Serve as the go-to People partner for employees and managers, responding to questions and concerns with care, speed, discretion, and sound judgment.
  • Shape and reinforce a high-performance, mission-driven culture as the company scales, preserving trust, intensity, and high standards.
  • Lead employee relations and navigate sensitive, high-stakes people situations with sound judgment, discretion, empathy, and appropriate risk awareness.
  • Oversee visa and immigration processes, partnering with external counsel and vendors to support employees through each stage of the process.
  • Own People-related compliance and risk management, ensuring policies, practices, and processes evolve appropriately as the company grows.
  • Communicate People-related updates and initiatives to the broader team in a clear, warm, consistent, and culturally aligned way.

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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Simplify Jobs

Simplify's Take

What believers are saying

  • Goodfire raised $150 million at a $1.25 billion valuation in February 2026.
  • Silico launched April 2026 and opened broadly in August 2026, expanding market reach.
  • Microsoft, Mayo Clinic, and Arc Institute validate demand beyond pure AI labs.

What critics are saying

  • Anthropic, OpenAI, and Google DeepMind internal teams can copy interpretability features quickly.
  • Silico’s long-horizon experiments are expensive, and bespoke pricing blocks broad self-serve adoption.
  • If mechanistic interpretability disappoints, Goodfire becomes a costly research lab, not infrastructure.

What makes Goodfire unique

  • Goodfire sells mechanistic interpretability, not generic AI tooling, through Silico and Ember.
  • Silico, publicly available August 2026, lets users inspect and edit neurons during training.
  • Goodfire already partners with Microsoft, Mayo Clinic, and Arc Institute on real deployments.

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Benefits

Company Equity

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

8%

2 year growth

-3%
MIT Technology Review
May 1st, 2026
Startup launches AI debugging tool to make LLM development more scientific

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.

MIT Technology Review
Apr 30th, 2026
Goodfire's Silico tool lets developers debug and adjust AI behaviour by manipulating individual neurons

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.

NetDynamic Web Services
Apr 30th, 2026
Goodfire launches Silico: A game-changer for LLM debugging.

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.

Caproasia
Feb 6th, 2026
US AI research lab Goodfire AI raises $150M Series B at $1.25B valuation

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.

PR Newswire
Feb 5th, 2026
AI Lab Goodfire Raises $150M at $1.25B Valuation to Design Models with Interpretability

/PRNewswire/ -- Today, Goodfire—the AI research lab using interpretability to understand, learn from, and design models—announced a $150 million Series B...