Hedra builds models that generate, perceive, and predict the visual world — and the platform that runs them in production. Backed by a16z and a16z speedrun.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
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 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
More about this role
Hedra is a research and infrastructure lab advancing post-training and inference for multimodal and physical AI. Our ambition is to bring recursive self-improvement to models that learn from and interact with the real world.
We combine reinforcement learning research with GPU compute and optimized inference to help teams post-train, serve and continuously improve models they own.
We're backed by Index Ventures, a16z and Abstract Ventures. Our team works fully in person in San Francisco.
We're looking for people with startup experience who take ownership of their work and care deeply about the problems our customers need to solve. You'll help build the technology, products and business that turn this research into a lasting company.
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,...
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