Senior/Principal Machine Learning Engineer

Who you are

• 8+ years of experience as a research-focused data systems engineer (preferably working with 3D and video foundation 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
• Experience with cloud data platforms and distributed processing technologies (e.g., Spark, Ray, Kubeflow, S3, etc.)
• 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
• MLOps Experience: Knowledge of experiment tracking (Weights & Biases, MLflow) and versioning for massive datasets
• Custom Tooling Development: Experience building internal "human-in-the-loop" tools for data labeling specific to video or 3D
• C++ Knowledge: Optimize the performance of data loaders and being comfortable modifying engine code
• Game development and digital content creation tools: Experience with making Roblox games, using Blender, Unreal Engine, or Unity

What the job involves

• 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 VideoGen and 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 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 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
• High-Scale Data Orchestration: Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D,) spanning petabytes of data
• Synthetic Data Generation: Leverage image and video generation models to scale multi-modal synthetic datasets
• Research-to-Production Bridge: Partner with research teams to create training data for research experiments – research and implement synthetic data creation pipelines
• 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
• Model Deployment & API Architecture: Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access
• Autonomous SOTA Tracking: Actively participate in literature reviews and paper reading groups to identify and implement the latest optimizations in generative modeling
• Resource Efficiency & Observability: Implement monitoring pipeline health, optimizing data loading to ensure GPUs are used efficiently