Applied Compute
Applied Compute builds enterprise AI systems that turn internal knowledge into custom models and deployable agent workforces. Its platform and forward-deployed teams create evals, train models on proprietary data, and run continually learning agents inside customer environments. The company is privately held and headquartered in San Francisco, serving enterprises that want in-house, domain-specific AI rather than generic foundation models.
Open Positions at Applied Compute
6 open positions
GTM Engineer building customer-facing AI demos, bespoke agents, and automation that support prospect engagements and accelerate sales cycles. The role combines rapid prototyping, technical deal support, and internal GTM tooling.
Build the full-stack platform, SDK, interfaces, and sandboxing tools that enable collaborative human-AI workspaces and agent development. This San Francisco-based role combines polished product engineering with LLM-powered applications and frontier AI research.
Build the polished product interfaces, visualizations, and design systems that make complex AI training and agent workflows intuitive. This hybrid design-and-front-end role works closely with the founding team and customers to shape both the product experience and public-facing brand.
Help define and build Applied Compute's platform security posture, from secure architecture and offensive testing to agent-powered defense and incident response. This role combines hands-on security engineering with ownership of enterprise security guarantees and the opportunity to shape security for AI agent deployments.
Build the AI systems that help enterprise agents learn, improve, and scale across customer deployments. You’ll combine software engineering, ML research, continual learning, and agent infrastructure to turn applied research into platform capabilities.
Build, train, evaluate, and deploy AI agents and custom models for enterprise customers while owning technical engagements end-to-end. This customer-facing role combines applied AI research, model post-training, and forward-deployed implementation.