Overview

We are building AI to simulate the world through merging art and science. We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error can be massively accelerated when done in simulation, rather than in the real world. World models offer the most clear path to general-purpose simulation, changing how stories are told, how scientific progress is made and how the next frontiers of humanity are reached.

Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you.

About the role

We’re looking for Research Engineers to help our world models train faster and run more efficiently, without compromising what they can do. You will profile, optimize, and rearchitect the systems that turn research ideas into models that run at scale and in real time — directly shaping what is computationally possible and, by extension, what capabilities we can build.

What you'll do

• Optimize training throughput across large GPU clusters — improving MFU through custom kernels, mixed-precision strategies (FP8, BF16), memory-efficient attention, and activation checkpointing
• Design and maintain distributed training infrastructure: tensor parallelism, context parallelism, FSDP, and fault-tolerant multi-node setups
• Profile and accelerate inference pipelines for real-time multimodal generation — CUDA graph compilation, KV cache optimization, operator fusion, and latency reduction
• Optimize and scale our training infrastructure to improve efficiency and reliability
• Contribute to the entire stack, from low-level kernel optimizations to high-level model design

What you'll need

• 4+ years of experience in systems engineering, ML infrastructure, or performance optimization for deep learning
• Familiarity with GPU kernel development (CUDA, Triton, CUTLASS) and distributed systems (NCCL, collective communication, model parallelism)
• Experience with ML framework internals (PyTorch, JAX) and mixed-precision / low-precision techniques (FP8, INT8)
• Experience building and operating large-scale training infrastructure, including fault tolerance and cluster orchestration
• Excitement about building AI that simulates the world — and making it performant enough to run in real time
• Bonus if you have experience with torch’s compilation feature

Working at [Employer hidden]

Great things come from great teams. We’d love to hear from you.

We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. So regardless of race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply.

More about [Employer hidden]:

• Universal World Simulator
• GWM-1
• Gen-4.5
• General World Models
• Robotics SDK
• Conversational Real-time Agents
• [Employer hidden] Studios

We're excited to be recognized as a best place to work: Crain's | InHerSight | BuiltIn NYC | INC

Compensation Range: $270K - $370K