Senior Machine Learning Engineer, GenAI Data

Roblox•San Mateo, CA

13d•$243,290 - $295,250•Onsite

About The Position

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Senior Software 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.

Requirements

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

Nice To Haves

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.

Responsibilities

Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D,) spanning petabytes of data
Leverage image and video generation models to scale multi-modal synthetic datasets
Partner with research teams to create training data for research experiments – research and implement synthetic data creation pipelines
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
Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access
Actively participate in literature reviews and paper reading groups to identify and implement the latest optimizations in generative modeling
Implement monitoring pipeline health, optimizing data loading to ensure GPUs are used efficiently