Job Description

Machine Learning Systems Engineer – 3D Generative AI

A well-funded and fast-growing company at the forefront of 3D generative AI is seeking a Machine Learning Systems Engineer to help build the most advanced end-to-end machine learning infrastructure for 3D content generation. This is a rare opportunity to work alongside a globally distributed team of experts in computer graphics, AI, and large-scale systems.

With millions of users and tens of millions of 3D assets generated, the platform is transforming the way creators—from solo artists to game developers—bring ideas to life in 3D. Backed by top-tier venture capital and led by researchers with backgrounds in elite institutions and industry-leading tech companies, this team is pioneering next-generation 3D foundation models.

What You’ll Do

You’ll be responsible for helping design, build, and optimize the machine learning infrastructure supporting large-scale 3D model training and inference. This includes:

Training Infrastructure

• Collaborate with researchers to support training of custom in-house foundational models.
• Build and scale distributed training systems across hundreds or thousands of GPUs.
• Identify bottlenecks and optimize throughput and efficiency.
• Develop and maintain job scheduling systems and GPU clusters.
• Create and optimize custom 3D-specific operations using Triton or CUDA.

Inference Systems

• Develop and maintain efficient inference endpoints for complex model pipelines.
• Implement model optimization strategies such as compilation, kernel fusion, quantization, and more.
• Enable low-latency inference for real-time or near-real-time applications.

What We're Looking For

• Solid background in machine learning and/or high-performance graphics .
• Strong coding skills in Python and/or C++ .
• Experience with ML frameworks such as PyTorch , JAX , or Flax .
• Passion for digging into low-level performance improvements and system optimization.
• Ability to rapidly learn new technologies and work in a fast-paced, collaborative environment.
• Excellent communication skills and experience working with remote teams.

Nice To Have

• Deep understanding of PyTorch internals, including experience with torch.compile, FSDP, or distributed training backends.
• Familiarity with Triton kernel development.
• Experience with large-scale model parallelism techniques: data, tensor, pipeline, or hybrid parallelism.
• Hands-on experience with 3D generative models , diffusion models , or video-based models .
• Knowledge of mixed precision training (e.g., bf16, fp8).

This is a high-ownership role with the opportunity to shape the future of 3D content generation. If you’re excited about building ML systems at scale and working on cutting-edge 3D AI, this could be a great fit.

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