Machine Learning Engineer (Systems)

Salary: $150k to $250k Equity: Up to 1% equity On-site work policy Sunnyvale, CA onsite with some flexibility on a case-by-case basis. Full-time position Location Saratoga, California, San Francisco Bay Area, California Visa sponsorship details Open to visa transfers (e.g. OPT, H1B transfers) Additional visa sponsorship details H1B transfers, new H1B applications, TN visas Us citizen and permanent residents 5+ years of experience 5+ years in ML systems engineering in production (academic years can substitute if PhD from top institution in relevant ML systems field) Tech stack LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, Google ADK, vLLM, SGLang, Ray, NVIDIA Triton, NVIDIA Dynamo, Distributed Systems, Software Defined Networking, Docker, Kubernetes, Python, APIs, Cloud Infrastructure, Tableau, Grafana, MCP About The Role We are looking for a full-stack ML systems engineer. This is a SENIOR position and you will to work alongside the Chief Architect and the CEO to turn ML systems research into a product that developers and enterprises enjoy using. You are a good fit if: * You have 5+ years of professional software engineering experience. * You have developed multi-agentic systems using multiple tool stacks e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom agent systems. * PLUS: You have deployed multi-agentic systems into live production * You have used model-serving platforms e.g., vLLM, SGLang, Ray, NVIDIA Dynamo * You have strong full-stack development skills. Especially front-end, including visualization via Tableau, Grafana etc. * You have used and developed-upon open source software * PLUS:You have contributed to open-source software * You have good product instincts: you can take a powerful backend capability and make it legible, useful, and satisfying for developers. * PLUS: You have worked as Solutions Architect or a Forward Deployment Engineer * You have experience with APIs, distributed systems, async jobs, queues, containers, deployment systems, or cloud infrastructure. * You are self-driven and can thrive in ill-defined, ambiguous, dynamic, early-stage work. * You possess clear written communication. You will help turn research-grade ideas into docs, examples, onboarding flows, and product language. Work experience Has hands- on experience with agentic systems - built multi- agent applications using LangChain, LangGraph, ADK, etc. ; understands agent scaling Hard skills Full- stack development in Python LangChain, LangGraph, ADK, CrewAI, AutoGen, or equivalent. Baseline Seniority 5+ years of experience 5+ years in ML systems engineering in production (academic years can substitute if PhD from top institution in relevant ML systems field) Work experience Has seen and dealt with scale, either on the orchestration side (what agents need to scale) or the infrastructure side (how to actually scale AI compute). *Nice-to-have* Work experience Worked at agent dev or inference provider companies Built products sold to enterprise buyers (CTO/CIO) Experience with products that have an open- source core + managed layer on top Startup or fast- paced environment experience *Education * PhD or MS from strong program in ML systems substitutes for experience *Hard skills* Distributed systems, Ray, or software- defined networking knowledge Familiarity with vLLM, SGLang, Nvidia Triton, or equivalent Experience with containers, cloud infra, and deployment systems Open- source contributions or development experience Role requirements Seniority 5+ years of experience 5+ years in ML systems engineering in production (academic years can substitute if PhD from top institution in relevant ML systems field) Work experience Has seen and dealt with scale, either on the orchestration side (what agents need to scale) or the infrastructure side (how to actually scale AI compute). Worked at agent dev or inference provider companies Built products sold to enterprise buyers (CTO/CIO) Experience with products that have an open- source core + managed layer on top Startup or fast- paced environment experience Education PhD or MS from strong program in ML systems substitutes for experience Hard skills Distributed systems, Ray, or software- defined networking knowledge Familiarity with vLLM, SGLang, Nvidia Triton, or equivalent Experience with containers, cloud infra, and deployment systems Open- source contributions or development experience Pay: $150,000.00 - $250,000.00 per year Work Location: In person