ML Infrastructure Engineer

• Set up and scale inference/Ray Serve for ML and LLM model serving, integrated with our data analysis and agent workflows
• Scale agent GPU infrastructure for concurrency and efficiency across multiple agent workloads
• Optimize and improve the engine builder and model server that power scalable agent orchestration

• Proven ability to build scalable ML/AI platforms from scratch, end-to-end, for production use cases. You've owned a zero-to-one build before, or can show you're capable of it
• Deep understanding of the inference stack: vLLM, KV cache, and the optimization layers underneath model serving
• Experience building distributed systems for AI/ML workloads at scale, connecting them to real product or vertical integrations
• 3+ years of relevant experience. We care about capability, not tenure

• Ray / Ray Serve experience
• Familiarity with AIBrix

• A rare chance to shape both company and product direction as an early team engineer
• Work alongside engineers and researchers from LinkedIn, Visa, Meta, and Branch
• Onsite culture in San Mateo, built for deep collaboration and high-velocity building
• Full benefits (medical, dental, vision, 401k)
• We sponsor H-1B visas and assist with immigration

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.