As a Machine Learning Engineer on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time.

In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity 3D data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive 3D datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality data, thereby supporting the next generation of creative AI.
You will:

1. High-Scale Data Orchestration: Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of 3D datasets spanning petabytes of data
2. Synthetic Data Generation: Research and implement synthetic data creation pipelines.
3. Research-to-Production Bridge: Collaborate closely with ML Engineers and Data Scientists to understand their data requirements, build tooling that streamlines their data workflows, and troubleshoot data-related system issues
4. Scalable Evaluation Frameworks: Build and own evaluation-automating both heuristic-based metrics and human-in-the-loop interfaces to evaluate and benchmark training datasets and in-house foundation models
5. Model Deployment & API Architecture: Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access
6. Autonomous SOTA Tracking: Actively participate in literature reviews and paper reading groups to identify and implement the latest optimizations in generative modeling
7. Resource Efficiency & Observability: Implement monitoring pipeline health, optimizing data loading to ensure GPUs are used efficiently

You have:

• 5+ years of experience as a research-focused data systems engineer (preferably working with 3D generative models)
• Expertise in building scalable ML data pipelines for both batch and real-time environments. Experience working with and processing very large datasets (Petabytes or more)
• Versatile: You're a generalist and you are comfortable with several languages and technologies already; you are adaptable in any situation
• Team-Player & Technical Leader: You are a collaborative team member who actively mentors peers, drives technical excellence, and takes ownership of leading and delivering key features and projects across team boundaries
• Python Proficiency: You can write high-quality Python code for automation, tooling, and infrastructure management
• Are passionate about the potential of generative AI, particularly in creative domains like 3D/4D content.
• A Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or a similar technical field

Nice to have:

• MLOps & Data Versioning Expertise: Experience with experiment tracking platforms (e.g., Weights & Biases, MLflow) and managing versioning for massive-scale datasets.
• Custom Tooling & Annotation Infrastructure: Proven track record of developing internal human-in-the-loop workflows and annotation tooling tailored to 3D or video data.
• Low-Level Optimization (C++): Strong C++ proficiency to optimize data-loader performance and extend underlying core engine functionality.
• Game Engines & DCC Tools: Hands-on experience developing in Roblox Studio or working with digital content creation tools such as Blender, Unreal Engine, or Unity.

For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.

Annual Salary Range

$212,490-$295,250 USD

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).