Senior Inference Optimization Engineer - Dragonfly Portfolio

Location: United States (Remote)

Department: Portfolio

Location Type: HYBRID

Employment Type: FULL_TIME

• 5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge
• Hands-on experience with at least one production LLM inference engine (vLLM, SGLang) running at high volume
• Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile
• Experience with distributed inference strategies: tensor parallelism, pipeline parallelism, MoE parallelism in multi-GPU and multi-node environments
• GPU profiling fluency: Nsight Systems, Nsight Compute, PyTorch Profiler
• Proficiency in Python, Rust, or Go. C++/CUDA a strong plus
• Bonus: custom Triton kernels, diffusion/image model inference optimization, open-source inference framework contributions

• Stand up and optimize GPU infrastructure including B300 nodes in owned data centers
• Drive down TTFT and TPOT, push throughput, and improve cost per token for LLM inference workloads
• Build reproducible benchmarking harnesses across inference engines to identify optimal engine, quantization scheme, and parallelism strategy per workload and GPU SKU
• Optimize multivariate inference load-balancing algorithms within the inference routing system
• Evaluate emerging inference optimization techniques including custom CUDA/Triton kernels, novel attention variants, new quantization schemes, and compilation stack improvements
• Evaluate emerging inference hardware (FPGAs, ASICs, custom silicon) for viability in the stack

• We'll review your application and assess fit for this role.
• If there's a match, we'll facilitate a warm introduction to the team.
• If the timing isn't right, we'll keep you in mind for future opportunities across the portfolio.