Machine Learning Data Engineer, Research - 3D Data Focus at StabilityAi

, , United States -

Full Time

Start Date

Immediate

Expiry Date

29 Jan, 26

Salary

0.0

Posted On

31 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

3D Graphics, Data Infrastructure, Machine Learning, Data Quality, Shader Development, Image Processing, Python, Cloud Storage, Automation, Computer Vision, Generative AI, Data Curation, Team Collaboration, Data Management, Scripting, Data Warehousing

Industry

Research Services

Description

(North America remote) ML Data Engineer, Research - 3D Data Focus About the role: We are looking for a talented ML Data Engineer, Research, with a background in 3D computer graphics, to join our Data team. You will work alongside a growing multidisciplinary team of talented research scientists and machine learning engineers to improve and scale the efficiency within our models. You will contribute to building the data infrastructure that manages our data and powers the training of all Stability AI models (especially 3D generative models). The position is full-time and 100% remote. Responsibilities: Ingest, clean, normalize, and preprocess data in a scalable, parallelizable way to prepare it for our machine learning model training pipelines while ensuring data quality Design, implement, and maintain scalable data infrastructure for generative AI Develop tools to search, curate, and serve the data at scale Collaborate with research teams to understand and meet their data requirements Maintain and improve data quality and integrity across various databases and data stores Manage and organize large-scale unstructured data, including image, text, audio, video and 3D Keep up to date with the latest data trends and research papers and implement novel ways to create synthetic data Qualifications: Background in real-time or offline 3D graphics and rendering (either academic or industry) Experience with 3D technologies such as Blender, Houdini, 3ds Max, Maya, and with concepts such as PBR materials and tools for PBR editing (e.g., Substance 3D) Experience with shader development (especially for offline rendering) Experience with common high-end 3D renderers (e.g., Cycles, VRay, Octane, Renderman, Arnold, Corona) Experience with procedural 3D data generation Experience with real-time 3D graphics and shader APIs programming (e.g., OpenGL, Vulkan, Direct3D, GLSL, HLSL) Experience with image processing and computer vision methods and frameworks (e.g., Open3D, OpenCV) Experience in dealing with a diverse range of 3D geometry representations and file formats (e.g., triangular or quad meshes, voxels, NeRFs, SDFs, pointclouds, etc) Experience with a diverse range of 2D image file formats (e..g, 16/32bit image depth, HDR data in formats such as OpenEXR). Experience with automation and scripting for 3D asset pipelines (e.g., Blender BPY, MEL, HScript) Strong understanding of modern 3D file formats such as USD and glTF Proven background within large-scale distributed workloads, and multi-component pipeline synchronization and orchestration Experience fine tuning large generative models (e.g., diffusion, language, or multimodal) on domain-specific datasets Experience with cloud storage and file systems. AWS (S3) is strongly preferred, but open to other cloud platforms Experience with Python, and additional languages such as C/C++, JavaScript Expertise in data warehouses and lakehouse technologies (Postgres, MongoDB, BigQuery, Delta Lake, Iceberg, etc ). Experience working with AI research teams and curating data for them Good teamwork and communication skills based on experience working with a distributed international team with timezone and cultural differences Experience working effectively remotely Excellent communication skills to effectively collaborate with users, solve issues, and provide guidance Attention to detail and the ability to document processes and solutions effectively Equal Employment Opportunity: We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

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Responsibilities

Ingest, clean, normalize, and preprocess data for machine learning model training pipelines while ensuring data quality. Collaborate with research teams to understand and meet their data requirements and maintain large-scale unstructured data.