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Hedra is building the platform, models, and infrastructure for visual intelligence. The company is a small, highly technical team in San Francisco backed by a16z and other leading investors, where researchers and engineers work closely across boundaries, own problems end-to-end, and have significant influence over what and how they build.
As an Inference Optimization Engineer, you will work at the boundary between research and systems, taking state-of-the-art visual model architectures and figuring out how to run them efficiently on modern hardware. You'll own the full stack of optimization—from understanding where time and memory are spent, to identifying algorithmic and systems-level improvements, to implementing optimizations across model architecture, inference algorithms, runtimes, kernels, and distributed execution.
Key responsibilities include:
- 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
- Optimize model execution across single-GPU, multi-GPU, and multi-node environments
- Reason about the interaction between model architecture and hardware, and collaborate with researchers on architectural changes that unlock performance improvements
- Investigate communication, memory movement, parallelism, and distributed execution strategies for large visual models
- Build rigorous benchmarking, profiling, and performance-regression infrastructure
- Evaluate new inference runtimes, compilers, frameworks, optimization techniques, and accelerator hardware
- Stay current with advances in efficient inference, GPU programming, model architectures, compilers, and ML systems research
- Help turn research breakthroughs into models that can be deployed and served efficiently at scale
The company values technical depth, curiosity, and demonstrated ability over years of experience. They are open to experienced ML systems engineers as well as exceptional early-career engineers or researchers who have already gone unusually deep on model performance, GPU systems, or efficient inference.
REQUIREMENTS:
- 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 work
- 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 willingness to modify or rethink existing approaches when the performance problem calls for it
- Comfort working on ambiguous problems where the bottleneck and sometimes even the right question is not known in advance
- Ability to communicate technical ideas clearly and collaborate closely with research scientists and engineers
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
- Experience optimizing diffusion, autoregressive, transformer, or other large generative architectures
- Multi-GPU or multi-node model execution, including tensor, pipeline, sequence, or other forms of parallelism
- Experience optimizing communication or data movement between accelerators
- Experience with profiling tools such as Nsight Systems or Nsight Compute
- Contributions to ML systems, inference runtimes, compilers, GPU libraries, or performance-focused open-source projects
- Research or publications in efficient ML, ML systems, GPU computing, compilers, or related areas
