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 this role
We are seeking a Product-Minded Junior Research Infrastructure Engineer to join our growing team. This is a \"70/30\" role: you will spend 70% of your time on hardcore backend and infrastructure—tackling complex distributed systems—and 30% of your time building intuitive internal tools that transform our platform capabilities into a seamless product experience for researchers. You will design, build, and operate distributed data systems that power large-scale ingestion, processing, and transformation of datasets used for AI model training.
This is a versatile role: you'll own end-to-end pipelines, ensure data quality and scalability, and collaborate closely with ML researchers to prepare diverse datasets for cutting-edge model training. You'll thrive in our fast-paced startup environment, where problem-solving, adaptability, and wearing multiple hats are the norm.
Key Responsibilities
Distributed Systems & Orchestration (70% - Core Engineering)
Participate in the design and implementation of distributed task orchestration systems using Temporal or Celery.
Architect pipelines across cloud object storage (S3, GCS), data lakes, and metadata catalogs.
Implement partitioning, sharding, and caching strategies to ensure data processing pipelines are resilient, highly available, and consistent.
Core Data Pipelines
Design, implement, and maintain distributed ingestion pipelines for structured and unstructured data (images, 3D/2D assets, binaries).
Build scalable ETL/ELT workflows to transform, validate, and enrich datasets for AI/ML model training and analytics.
Pretrain Data Processing
Support preprocessing of unstructured assets (e.g., images, 3D/2D models, video) for training pipelines, including format conversion, normalization, augmentation, and metadata extraction.
Implement validation and quality checks to ensure datasets meet ML training requirements.
Collaborate with ML researchers to quickly adapt pipelines to evolving pretraining and evaluation needs.
Infrastructure & DevOps
Use infrastructure-as-code (Terraform, Kubernetes, etc.) to manage scalable and reproducible environments.
Manage data assets using Databricks Asset Bundles (DABs) and build rigorous CI/CD pipelines (GitHub Actions).
Focus on maximizing cluster utilization (CPU/Memory) and optimizing EC2 instance allocation to aggressively reduce compute costs.
