Inference Optimization Engineer

Hedra is a platform, models, and infrastructure company focused on visual intelligence. The Inference Optimization Engineer will work with research scientists and engineers to improve the speed, efficiency, and scalability of visual model inference across architectures, algorithms, runtimes, GPU kernels, and distributed systems.

Responsibilities:

• 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
• Build rigorous benchmarking, profiling, and performance-regression infrastructure to understand performance and evaluate new optimization ideas
• Evaluate new inference runtimes, compilers, frameworks, optimization techniques, and accelerator hardware
• Stay close to advances in efficient inference, GPU programming, model architectures, compilers, and ML systems research, and rapidly test promising ideas
• Help turn research breakthroughs into models that can be deployed and served efficiently at scale