Job Url: https://simplify.jobs/p/3b22588a-d09d-4d3b-8dd7-bd18e0932ea3/Member-of-Technical-Staff Job Description: On-device foundation models for edge AI No salary listed Senior Remote in USA + 2 more More locations: Boston, MA, USA | San Francisco, CA, USA Remote Category Software Engineering (1) Backend Engineering Required Skills Skills that you prefer have been highlighted gRPC Kubernetes Microsoft Azure Python Grafana Node.js SQL Machine Learning Docker Microservices AWS Go Redis MongoDB C/C++ Development Operations (DevOps) Google Cloud Platform Git Java Postgres Scala Prometheus Terraform REST APIs Get referrals → You have ways to get a Liquid AI referral from your network. Applications through a referral are 3x more likely to get an interview! History 2 events CLOSED Similar job posting Member of Technical Staff at Liquid AI Dec 09, 2025 · 02:58 PM OPENED Similar job posting Member of Technical Staff at Liquid AI Oct 30, 2025 · 11:11 PM Summary Full Job Posting Why This Job is a Match See more like this? Yes No Matched based on your job preferences Company size preferences for 51-200 Industries preferences for Data & Analytics, Enterprise Software, AI & Machine Learning Skills preferences for AWS, Microsoft Azure, Docker, Kubernetes, Go, C/C++, Development Operations (DevOps), Google Cloud Platform, Grafana, gRPC, Machine Learning, Microservices, MongoDB, Node.js, Redis, SQL Category preferences for Software Engineering Change preferences → Work With Us At Liquid, we’re not just building AI models—we’re redefining the architecture of intelligence itself. Spun out of MIT, our mission is to build efficient AI systems at every scale. Our Liquid Foundation Models (LFMs) operate where others can’t: on-device, at the edge, under real-time constraints. We’re not iterating on old ideas—we’re architecting what comes next. We believe great talent powers great technology. The Liquid team is a community of world-class engineers, researchers, and builders creating the next generation of AI. Whether you're helping shape model architectures, scaling our dev platforms, or enabling enterprise deployments—your work will directly shape the frontier of intelligent systems. While San Francisco and Boston are preferred, we are open to other locations in United States. This Role Is For You If: You thrive on building systems from the ground up, not just maintaining them You understand that good infrastructure is the difference between theoretical and practical ML You balance pragmatism and ambition—knowing when to push for scale and when to ship You’re excited by the challenge of turning complex ML capabilities into intuitive products Desired Experience: Proven track record of architecting and scaling systems from scratch Deep understanding of modern software architecture and best practices Experience deploying ML-powered systems in real-world production environments Strong opinions about engineering practices—backed by hard-earned lessons Ability to decide when to build vs. when to leverage existing tools What You'll Actually Do: Architect and implement full-stack solutions for both internal platforms and customer-facing products Design and scale backend services that enable robust ML model deployment Build deployment infrastructure that runs seamlessly across cloud and on-premise environments Develop interfaces that make complex ML systems accessible and usable Establish workflows that accelerate ML research-to-deployment cycles Collaborate closely with Product and ML teams to iterate quickly and effectively What You'll Gain: The chance to architect foundational systems at a true greenfield stage Direct collaboration with exceptional ML researchers and product builders Influence over critical technical decisions that will define Liquid’s trajectory The opportunity to shape how enterprises deploy efficient AI models at scale About Liquid AI Spun out of MIT CSAIL, we’re a foundation model company headquartered in Boston. Our mission is to build capable and efficient general-purpose AI systems at every scale—from phones and vehicles to enterprise servers and embedded chips. Our models are designed to run where others stall: on CPUs, with low latency, minimal memory, and maximum reliability. We’re already partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services. And we’re just getting started.