Meshy is a company building foundation models for AI-powered 3D generation that produce production-ready, printable, and game-ready 3D models from text or images.
ABOUT MESHY Headquartered in Silicon Valley, Meshy is the leading 3D generative AI company on a mission to Unleash 3D Creativity by transforming the content creation pipeline. Meshy makes it effortless for both professional artists and hobbyists to create unique 3D assets—turning text and images into stunning 3D models in just minutes. What once took weeks and cost $1,000 now takes just 2 minutes and $1. Our world-class team of top experts in computer graphics, AI, and art includes alumni from MIT, Stanford, and Berkeley, as well as veterans from Nvidia and Microsoft. Our talent spans the globe, with team members distributed across North America, Asia, and Oceania, fostering a diverse and innovative multi-regional culture focused on solving global 3D challenges. Meshy is trusted by top developers, backed by premiere venture capital firms like Sequoia and GGV, and has successfully raised $52 Million in funding. Meshy is the market leader, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games) and No.1 in website traffic (according to SimilarWeb, with 3 Million monthly visits). The platform boasts over 5 Million users and has generated 40 Million models. Founder and CEO Yuanming (Ethan) Hu earned his Ph.D. in graphics and AI from MIT, where he developed the acclaimed Taichi GPU programming language (27K stars on GitHub, used by 300+ institutes). His work is highly influential, including an honorable mention for the SIGGRAPH 2022 Outstanding Doctoral Dissertation Award and over 2,700 research citations. ABOUT THE ROLE - This role sits at the intersection of platform engineering, site reliability, and applied ML systems. The function owns the reliability, scalability, and operability of Meshy's AI model serving stack, along with core engineering infrastructure. The team operates a conventional production infrastructure (CI/CD, build systems, deployment, runtime environments) and develops a model-serving platform that connects the models developed by our Research Team to product-facing backend systems. The position is systems-heavy, production-oriented, and focused on turning experimental model artifacts into robust, observable, and cost-efficient services. JOB RESPONSIBILITIES - Responsible for the design, development, and optimization of core capabilities for the AI inference platform, including key modules such as inference services, task scheduling, service orchestration, elastic scaling, and release governance. - Participate in the development of CPU/GPU resource management systems to optimize stability, resource utilization, and cost efficiency in scenarios where online inference and training tasks are run on the same cluster. - Drive the unified management and scheduling of GPU resources, and explore the practical implementation of capabilities such as MIG, MPS, time-sharing, and virtualization in real-world business operations. - Continuously optimize the throughput, latency, and availability of the inference pipeline, refining engineering quality in complex inference pipelines, multi-model collaboration, and high-concurrency scenarios. - Focus on R&D efficiency, resource and cost management, online stability, and disaster recovery architecture design to drive the company’s continuous evolution in performance, reliability, and maintainability. - Explore AI-native infrastructure and automated operations to make infrastructure smarter and more user-friendly, supporting the company’s rapid expansion during its startup phase. QUALIFICATIONS - Bachelor’s degree or higher; majors in Computer Science, Software Engineering, Artificial Intelligence, Telecommunications, or related fields are preferred. - 1 to 3 years of experience in backend development, infrastructure, cloud-native platforms, machine learning platforms, or AI platforms. - Proficiency in at least one of Go or Python, with solid software engineering skills and a strong commitment to code quality. - Understanding of fundamental principles in Linux, operating systems, computer networks, and distributed systems; ability to independently identify and resolve complex engineering issues. - Practical development experience with Kubernetes, Docker, microservices, or distributed systems, with a basic understanding of production system stability. - Real-world project experience in areas such as model inference, task orchestration, resource scheduling, and service stability—beyond mere conceptual understanding. - Self-motivated, curious, and a fast learner; willing to take on greater ownership and broader responsibilities in a startup environment, while continuously learning and quickly adopting new technologies. NICE TO HAVE - Experience with GPU inference platforms, Kubernetes schedulers, Device Plugins, or related platform development. - Familiarity with frameworks such as Ray and Ray Serve, or experience in developing and optimizing model serving, distributed inference, and task orchestration frameworks. - Familiarity with solutions related to MIG, MPS, vGPU, partitioned GPUs, or GPU resource reuse, and experience balancing performance and stability. - Engineering experience in observability, SRE, capacity planning, cost governance, canary deployments, and automated rollbacks. - Open-source projects, technical blogs, personal projects (side projects), or other achievements that demonstrate learning agility and growth potential. - Ongoing interest and hands-on experience in emerging areas such as AI infrastructure (AI Infra), inference systems, and AI agent toolchains. OUR VALUES - Brain: We value intelligence and the pursuit of knowledge. Our team is composed of some of the brightest minds in the industry. - Heart: We care deeply about our work, our users, and each other. Empathy and passion drive us forward. - Gut: We trust our instincts and are not afraid to take bold risks. Innovation requires courage. - Taste: We have a keen eye fo