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We build Specific Intelligence for the enterprise: agents that continuously learn from a company's processes, data, expertise, and goals. We're building the continual learning layer and platform that captures context, memory, and decision traces across the enterprise, providing an environment where specialized agents learn how to do real work.
Why we're excited: We get to work at a rare intersection. Our product team builds the platform powering a new generation of digital coworkers. Our research team pushes the frontier of post-training and reinforcement learning to create new product experiences. Our applied research engineers sit side-by-side with customers as they ship agents into production. This combination of strong product, deep research, and boots on the ground is what we believe it takes to bring AI to the enterprise. We are product-led, research-enabled, and forward-deployed.
Our Team : We are a team of engineers, researchers, and operators. Many of us are former founders. We've built RL infrastructure at OpenAI, data foundations at Scale AI, and systems at Together, Two Sigma, Watershed, and other teams. We work with F50 customers, and we’re fortunate to be backed by Kleiner Perkins, Benchmark, Sequoia, Lux, Greenoaks, and others.
Who Thrives Here : We're looking for people who are excited about applying novel research and complex systems to real-world problems. You should be comfortable navigating unfamiliar environments quickly, whether that's a new codebase, a new customer's data architecture, or a problem domain you've never seen before. Our team genuinely enjoys working with customers: listening, empathizing, and understanding how work actually gets done in their organizations. Former founders, people who've built a lot of side projects, or anyone who's shown they can own something end-to-end, tend to do well here.
As a software engineer, you'll build the products and interfaces that customers and internal teams use every day. You'll own the full stack of our application platform, from the system that powers collaborative human-AI workspaces, the backend workflows that orchestrate sandboxed agent sessions, to the continual learning SDK that gives engineers control and visibility over the entire agent development lifecycle.
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San Francisco, USA
San Francisco, USA
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San Francisco, USA
San Francisco, USA
Build and ship applications end-to-end across the full stack: React frontends, Python and Temporal backends, Kubernetes clusters, SDK integrations, etc.
Design and implement the platform console: dashboards, resource management views, and observability UIs (e.g. trace inspection, experiment management, training run visualization)
Develop the AC SDK that internal and customer-facing engineers use to build domain-specific agents and applications
Build shared UI components, design systems, and interaction patterns across the platform
Collaborate with the Applied AI team to build for customer deployments and upstream learnings back into the platform
Collaborate with AI product engineers and infra engineers to integrate new platform capabilities (e.g. memory, context, browser) into polished user experiences
Strong full-stack engineering skills with experience shipping production web applications (React/TypeScript, Python)
Experience with both real-time (SSE, WebSockets, streaming UIs) and asynchronous (Temporal, Airflow, etc.) systems
Ability to build and iterate on polished, functional applications quickly with high engineering craft
Comfort working across the stack including frontend components, backend APIs, data models, and deployment
Strong product and design intuition: you care deeply about what the user actually experiences
Familiarity with LLM-powered applications and agent architectures
Experience building developer SDKs, frameworks, or extensible application platforms
Experience building observability or data-intensive dashboards (traces, logs, metrics visualization)
Previous experience as a founder or early engineer at a zero-to-one company
Enterprise AI infrastructure company helping businesses train, deploy, and continuously improve custom models and in-house agent workforces.
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