AI Platform Engineer
Applied Compute3 months ago
Base Salary
$190k - $310k/yr
Responsibilities
- Own the full stack of the AC2 platform.
- Integrate capabilities such as memory, context, and auto-research into platform experiences.
- Design consoles, dashboards, resource-management views, and observability interfaces for interacting with agents.
- Build tools that help subject matter experts capture and communicate workflows.
- Develop the AC SDK for internal and customer-facing engineers building domain-specific agents and applications.
- Build agent-sandboxing user experiences and deployment surfaces for different environments.
- Work with product research engineers and applied research engineers on platform capabilities and customer deployments.
Requirements
- Strong full-stack engineering skills and experience shipping production web applications.
- Deep familiarity with LLM-powered applications and agent architectures.
- Experience building polished, functional applications quickly with strong engineering craft.
- Experience with both real-time and asynchronous systems.
- Creativity, prototyping ability, and a strong focus on user experience.
- Experience with reinforcement learning from human feedback or similar human-in-the-loop learning systems is preferred.
- Previous experience as a founder or early engineer at a zero-to-one company is preferred.
- Published work or open-source contributions in AI/ML systems are preferred.
Benefits
- U.S. base salary range of $190,000–$310,000 plus meaningful equity.
- 100% employer-paid health insurance for the employee and family.
- Unlimited paid time off and paid parental leave.
- Daily lunches and dinners.
- Transportation and relocation support.
- 5% 401(k) match.
- Full-time role based in the San Francisco Mission office.
- Visa sponsorship is available with the stated qualification that success cannot be guaranteed for every candidate or role.
Categories
About 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.