AI Platform Engineer
Applied Compute3 months ago
Base Salary
$190k - $310k/yr
Responsibilities
- Own the full stack of the AC2 application platform.
- Integrate platform capabilities such as memory, context, and auto-research into user experiences.
- Design interfaces for interacting with agents, including consoles, dashboards, resource-management views, and observability UIs.
- Build tools for subject matter experts to 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 operation across different environments.
- Collaborate 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 with real-time and asynchronous systems.
- Ability to rapidly build and iterate on polished, functional applications with high engineering craft.
- Creativity, openness to prototyping, and strong attention to user experience.
- Experience with reinforcement learning from human feedback or similar human-in-the-loop learning systems is a strong-candidate qualification.
- Previous founder or zero-to-one early-engineer experience is a strong-candidate qualification.
- Published work or open-source contributions in AI/ML systems are a strong-candidate qualification.
Benefits
- Full-time role based in San Francisco with work from the company’s Mission office.
- $190,000–$310,000 U.S. base salary range plus meaningful equity.
- 100% employer-paid health insurance coverage.
- Unlimited paid time off.
- Paid parental leave.
- Daily lunches and dinners.
- Transportation and relocation support.
- Retirement plans.
- Visa sponsorship.
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.