Founding Engineer, ML Inference

reactor • San Francisco Bay Area
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AI Summary

We're seeking a highly technical Founding Engineer with expertise in high-performance ML engineering to drive real-time model performance for diffusion models. This role involves designing a high-performance in-house inference runtime and optimizing neural network models for inference. Collaboration with model partner teams is also essential.

Key Highlights
Real-time model performance for diffusion models
High-performance in-house inference runtime
Optimizing neural network models for inference
Key Responsibilities
Drive our frontier position on real-time model performance for diffusion models.
Design and implement a high-performance in-house inference runtime.
Implement optimizations using torch.compile, custom CUDA kernels, and specialized inference frameworks.
Technical Skills Required
PyTorch TensorRT TransformerEngine Nsight ONNX Runtime torch.compile CUDA kernels specialized inference frameworks
Benefits & Perks
Competitive San Francisco salary
Meaningful early equity
Health, dental, and vision coverage
Relocation support

Job Description


About Us

We're building a future where anyone can create interactive media applications that delight, educate, and simulate. Building a new kind of platform for real-time generative media, enabling developers to go from idea to immersive, dynamic experience in seconds. Join a small, focused team of YC and unicorn founders and senior engineers with deep expertise in 3D, generative video, developer platforms, and creative tool, aspiring to continuously push the boundaries of what's possible.


About the Role

We're looking for a Founding Engineer, ML Inference with deep expertise in high-performance ML engineering. This is a highly technical, high-impact role focused on squeezing every drop of performance from generative media models.

You'll work across the model-serving stack, designing novel inference frameworks, optimizing inference performance, and shaping the competitive edge in ultra-low-latency, high-throughput environments.


What You'll Do

  • Drive our frontier position on real-time model performance for diffusion models
  • Design and implement a high-performance in-house inference runtime
  • Implement optimizations using torch.compile, custom CUDA kernels, and specialized inference frameworks
  • Optimize neural network models for inference through quantization, pruning, and architectural modifications while maintaining accuracy
  • Profile and benchmark model performance to identify computational bottlenecks
  • Collaborate directly with model partner teams to directly integrate their models into our platform


Required Skills

  • Strong foundation in systems programming, with a track record of identifying and resolving bottlenecks
  • Deep expertise in the ML infrastructure stack: PyTorch, TensorRT, TransformerEngine, Nsight, ONNX Runtime
  • Model compilation, quantization (INT8/FP16), and advanced serving architectures
  • Working knowledge of GPU hardware (NVIDIA) and the ability to dive deep into the stack as needed
  • Strong understanding of transformer architectures and modern ML model optimization techniques


Logistics

We are based in-person in San Francisco. We believe the best ideas and work come from being together.


Benefits

• Competitive San Francisco salary and meaningful early equity.

• We sponsor visas. We are committed to working through the process together for the right candidates. If you're currently outside the US, we're also committed to helping you relocate to the US throughout this process.

• We offer generous health, dental, and vision coverage, and relocation support as needed.


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