AI Infrastructure Engineer

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.

Responsibilities

Build and scale AI inference infrastructure from the ground up, including inference serving, scheduling, orchestration, and auto-scaling; Design and optimize CPU/GPU resource management systems to maximize utilization and cost-efficiency; Drive the production-level implementation of GPU scheduling and multiplexing technologies (MIG, MPS, Virtualization); Optimize the inference pipeline (throughput, latency, and stability) to support high-concurrency and complex business scenarios; Contribute to system stability, disaster recovery design, and cloud cost management/governance; Explore AI-native infrastructure and automated O&M (Operations & Maintenance) to support rapid business growth.

Requirements

3–5 years of experience in Backend or Infrastructure engineering (Cloud-native or AI platform experience is highly preferred); Strong mastery of Go or Python with solid engineering foundations and clean coding habits; Deep understanding of Linux internals, networking, and distributed systems; Proven hands-on experience with Kubernetes, Docker, and microservices architecture; Prior project experience in inference systems, task scheduling, or resource management; Highly self-driven, quick learner, and eager to take on significant ownership in a fast-paced startup environment.

Nice to have

Experience with GPU platforms or customizing Kubernetes schedulers; Familiarity with Ray, model serving frameworks, or distributed inference architectures; Practical experience with GPU multiplexing technologies such as MIG, MPS, or vGPU; Engineering background in SRE, observability, or cloud cost optimization; Active contributor to open-source projects or possess significant technical influence in the community.

Benefits

Competitive salary, equity, and benefits package; Opportunity to work with a talented and passionate team at the forefront of AI and 3D technology; Flexible work environment, with options for remote and on-site work; Opportunities for fast professional growth and development; An inclusive culture that values creativity, innovation, and collaboration; Unlimited, flexible time off; Stock options available for core team members; 401(k) plan for employees; Comprehensive health, dental, and vision insurance; The latest and best office equipment.