Faros AI · San Francisco
OtherProductRemoteFullTimeposted 2mo ago
Software engineering is undergoing the largest transformation in its history.
As organizations invest heavily in AI for software development, they're facing a new set of questions: Where is AI actually being used? What impact is it having? Which investments are creating value? Where are teams getting stuck? And how do humans and AI work together effectively at scale?
Faros helps organizations answer those questions.
We sit at the center of the engineering operating system, connecting data across code, pull requests, deployments, work tracking systems, developer workflows, and AI tools. This gives us a uniquely rich view into how software is being built, how AI is being adopted, and what outcomes organizations are actually getting from those investments.
That same operational context is also what makes AI effective in large, complex enterprises. AI performs best when it understands the systems, people, processes, codebases, and business context surrounding a decision. Faros helps create and organize that context, enabling AI systems to operate with greater accuracy, relevance, and trust in environments that are often fragmented, messy, and difficult to navigate.
We believe the next generation of engineering organizations will be powered by a combination of human judgment, AI capabilities, and rich operational context. Faros is building the foundation that helps all three work together.
Our customers include some of the world's most sophisticated engineering organizations, including Autodesk, Discord, Coursera, and others.
We are looking for AI-Native Builders.
This is not a traditional software engineering role. We are not looking for people who simply use AI tools. We are looking for builders who understand that the scarce resource is no longer code — it is judgment.
As an AI-Native Builder, you will identify opportunities, rapidly test ideas, build products, automate workflows, create capabilities, design evaluation systems, and help define how engineering itself evolves in the age of AI.
You may build software products, internal platforms, agent workflows, evaluation frameworks, data systems, automation pipelines, prototypes, or entirely new capabilities that do not yet fit into a traditional organizational chart.
Success in this role is measured by outcomes, learning velocity, and leverage — not by lines of code written.
You will:
Build the intelligence layer that helps engineering organizations understand how work happens across people, code, systems, and AI
Create agents and workflows that use Faros's operational context to assist with engineering planning, delivery, review, quality, reliability, and decision-making
Design the context systems, memory, tooling, and retrieval capabilities that allow AI to operate accurately within the messy realities of enterprise software development
Build evaluation frameworks that measure the effectiveness of humans, AI, and human-AI systems against real engineering outcomes
Develop reusable platform capabilities that make it easier for customers to build, deploy, govern, and improve AI-powered engineering workflows
Rapidly experiment with emerging models, tools, and techniques, turning promising ideas into customer-facing capabilities
Use data and evidence to identify bottlenecks in software delivery and create products that help organizations continuously improve
Help define what great engineering looks like in a world where humans and AI build software together
Outcomes over activity
The goal is not token maxxing. The goal is not more lines of code, more prompts, or more pull requests. The goal is creating value.
Leverage over labor
As the cost of execution falls, the ability to create leverage becomes increasingly valuable. AI-native builders create systems, workflows, context, automation, and capabilities that amplify the output of people, teams, and agents.
Learning velocity over existing expertise
The half-life of knowledge continues to shrink. The people who thrive won't necessarily be those who know the most today, but those who can learn, adapt, and reconfigure themselves the fastest as the landscape changes.
Experiments over opinions
The cost of testing ideas has never been lower. When uncertainty exists, we should prefer evidence over debate. We learn by building.
Capabilities over interfaces
We're no longer building exclusively for humans. Agents should be treated as first-class citizens. Great builders create capabilities that can be composed, reused, automated, and leveraged by people, agents, workflows, and systems alike.
Systems over silos
AI lowers the cost of operating outside your specialty, making it possible for individuals to contribute across product, design, frontend, backend, data, and operations in ways that were previously impractical. The most effective builders will combine depth in one area with the ability to navigate many others.
Human judgment over human execution
Humans should increasingly spend their time deciding, evaluating, designing, and guiding. Machines should increasingly spend their time executing.
We care far more about evidence of exceptional building than specific credentials.
You will likely demonstrate many of the following:
Strong software engineering fundamentals
A history of building products, systems, tools, automation, workflows, or side projects
Demonstrated ability to learn new technologies quickly and independently
Experience using AI to accelerate development, research, analysis, or decision-making
Strong problem-solving skills and comfort operating in ambiguous environments
Ability to rapidly move from idea to implementation
Evidence of curiosity, initiative, and self-directed learning
Excellent written and verbal communication skills
Strong judgment and an ability to reason through tradeoffs
A bias toward action and experimentation
The strongest candidates often demonstrate one or more of the following:
Built systems, platforms, tools, or workflows adopted by teams or organizations
Designed AI agents, agentic workflows, evaluation frameworks, or AI-powered products
Built automation that significantly improved organizational productivity
Open-source contributions, hackathon projects, startups, research projects, or other evidence of self-directed building
Deep experimentation with modern AI models, agents, developer tools, or emerging technologies
Experience navigating major technology shifts and helping others adapt
A track record of identifying important opportunities before they become obvious