Machine Learning Systems Engineer

Location:

Sunnyvale, CA (Onsite with some flexibility)

Compensation: $150,000 – $230,000 + up to 1% equity

I am partnering with an early‑stage, deeply technical AI infrastructure startup building the technology to solve one of the hardest unsolved problems in AI: closing the gap between building agentic applications and actually scaling them efficiently. Their platform automates the entire workflow intelligence layer, providing policy‑driven scaling for multi‑agentic systems.

This is not another thin LLM wrapper. The core technology is backed by deep systems research involving distributed systems, software‑defined networking concepts, and OS‑level primitives applied to AI workflow orchestration.

As Employee #4, the successful candidate will work directly alongside a world‑class founding team—including a 3x founder (PhD, ex‑Bell Labs, ex‑Qualcomm) and a world‑renowned ML systems researcher and university chair professor.

Working closely with the CEO and Chief Architect, this engineer will turn complex ML systems research into a highly polished product that developers and enterprises enjoy using. This requires a rare blend of deep backend systems engineering, multi‑agent orchestration, and strong full‑stack visualization capabilities.

• Translate research‑grade backend capabilities into legible, useful, and satisfying product features for developers.
• Architect and scale multi‑agentic systems and model‑serving platforms.
• Assist with the development of documentation, onboarding flows, and product language to simplify complex technical concepts.

• 5+ years of professional software engineering experience.
• Hands‑on experience developing multi‑agentic systems using frameworks such as Lang Graph, Auto Gen, CrewAI, Semantic Kernel, or custom‑built agent systems.
• Experience utilizing model‑serving platforms like vLLM, SGLang, Ray, or NVIDIA Triton/Dynamo.
• Strong full‑stack development skills, with a specific emphasis on frontend visualization using tools like Grafana or Tableau.
• Strong background with APIs, distributed systems, async jobs, queues, containers, and cloud infrastructure.
• Self‑driven mindset with the ability to thrive in an ambiguous, early‑stage startup environment.

• Experience deploying multi‑agentic systems into a live production environment.
• Active contributions to open‑source software.
• Prior experience working as a Solutions Architect or Forward Deployment Engineer.

Python, Lang Graph, Lang Chain, Auto Gen, CrewAI, vLLM, SGLang, Ray, NVIDIA Triton/Dynamo, Distributed Systems, Software Defined Networking, Docker, Kubernetes, Cloud Infrastructure, Tableau, Grafana, MCP.