Inference Optimization Engineer

About the role

We’re looking for an Inference Optimization Engineer to work alongside our research team on making state-of-the-art visual models fast and efficient at inference time. Pro members see the company and apply now. It opens to everyone on November 7.

What you'll do

• Work directly with research scientists and engineers to make new visual models fast and efficient at inference time.
• Profile model architectures and workloads to understand bottlenecks across compute, memory, communication, and model execution.
• Develop and implement new approaches to improving inference latency, throughput, memory efficiency, and GPU utilization.
• Explore algorithmic optimizations including quantization, sparsity, caching, compilation, attention optimizations, and alternative execution strategies.
• Build or optimize GPU kernels using CUDA, Triton, or similar technologies when existing implementations leave performance on the table.
• Optimize model execution across single-GPU, multi-GPU, and multi-node environments.
• Reason about the interaction between model architecture and hardware, and work with researchers when architectural changes can unlock meaningful performance improvements.
• Investigate communication, memory movement, parallelism, and distributed execution strategies for large visual models.

What they're looking for

• Deep technical ability in efficient ML inference, ML systems, GPU computing, or adjacent research, demonstrated through research, production engineering, open-source contributions, or unusually ambitious independent...
• Strong understanding of how modern deep learning models execute on hardware, including the relationship between compute, memory, communication, and performance.
• Experience profiling ML workloads, identifying bottlenecks, forming hypotheses, and driving measurable performance improvements.
• Strong programming fundamentals in Python, C++, or another systems-oriented language.
• Experience with some combination of PyTorch, CUDA, Triton, TensorRT, vLLM, SGLang, or comparable technologies.
• Ability to reason across abstraction layers rather than treating model architecture, framework, runtime, kernel, and hardware boundaries as fixed.
• Strong intuition for performance tradeoffs across latency, throughput, memory, numerical precision, model quality, and complexity.
• Curiosity about how models work internally and a willingness to modify or rethink existing approaches when the performance problem calls for it.

Nice to have

• Experience optimizing large generative, multimodal, vision, or video models.
• CUDA, Triton, CUTLASS, or other GPU kernel development.
• Deep knowledge of GPU architecture, memory hierarchy, and hardware-aware optimization.
• Experience with attention optimization, kernel fusion, memory-efficient execution, or custom operators.
• Model compilation or graph optimization experience.
• Quantization, sparsity, caching, speculative execution, or other efficient inference techniques.