At World Labs, our mission is to revolutionize artificial intelligence by developing Large World Models, taking AI beyond language and 2D visuals into the realm of complex 3D environments, both virtual and real. We're the team that's envisioning a future where AI doesn't just process information but truly understands and interacts with the world around us.

We're looking for the overachievers, the visionaries, and the relentless innovators who aren't satisfied with the status quo. You know that person who's always dreaming up the next big breakthrough? That's us. And we want you to be part of it.

As a Model Optimization Engineer at World Labs, you will play a pivotal role in enhancing the performance and efficiency of large-scale foundation models. You will collaborate with Research Scientists to design, implement, and optimize systems that leverage thousands of GPUs to train state-of-the-art AI models. This position offers a unique opportunity to work at the forefront of AI technology, tackling complex challenges in PyTorch, CUDA, and distributed systems. Ensure efficient implementation of models and systems for data processing, training, inference, and deployment. Identify and implement optimization techniques for massively parallel and distributed systems. Profile and resolve efficiency bottlenecks (memory, speed, utilization) by developing high-performance CUDA, Triton, C++, and PyTorch code. Collaborate closely with the research team to integrate efficiency into system designs from the outset. Build tools to visualize, evaluate, and filter datasets to ensure quality and readiness for training. Implement cutting-edge prototypes and product features based on multimodal generative AI.

Key Qualifications:

• Programming Expertise:

  • Proficiency in Python and PyTorch with practical experience across the development pipeline: data processing, preparation, training, and inference.

  • Experience optimizing and deploying inference workloads with a focus on throughput and latency.

  • Skilled in profiling CPU and GPU code using tools such as Nvidia Nsight.

• Parallel and Distributed Systems:

  • Experience writing and improving parallel and distributed PyTorch code using techniques like DDP, FSDP, or Tensor Parallel.

  • Familiarity with high-performance parallel C++ in machine learning contexts (e.g., for data loading and processing).

  • Proficiency in Triton, CUDA, and writing custom PyTorch kernels, including tensor core utilization and memory optimization.

• Model Knowledge:

  • Understanding of deep learning architectures such as Transformers, Diffusion Models, and GANs.

  • Experience building prototype applications using tools like Gradio and Docker.

Preferred Qualifications

• Strong foundation in distributed computing and experience with large-scale AI training systems.

• Familiarity with multimodal generative models and emerging AI paradigms.

• Hands-on experience with dataset curation and visualization tools.

• Passion for collaborating with research teams to translate cutting-edge concepts into real-world solutions.

Who You Are:

• Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible.

• Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger.

• Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people.

• Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence.

We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad.

Join us, and let's make history together.