Principal Engineer, Model Performance

🔥 12 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

👻 Ghost score 25%

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Runware

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

🤖 Artificial Intelligence

🔌 API

📱 Media

Artificial Intelligence • API • Media

Runware is a flexible generative AI platform that specializes in high-quality media creation for images and videos through an affordable and fast API. It is capable of advanced tasks such as image generation, video inference, upscaling, and background removal, making it an essential tool for developers seeking to enhance their projects with AI technology. Powered by a custom Sonic Inference Engine™ and renewable energy, Runware ensures quick and efficient media generation without requiring complex infrastructure or machine learning expertise.

📋 Description

• Own inference performance end to end across kernels, serving engines, model-level optimizations and benchmarks for image, video, audio, 3D and LLM workloads • Set the model performance roadmap based on real traffic, cost and upcoming hardware • Write and tune CUDA kernels, fuse attention and MLP paths, introduce FP8, FP4 and INT4 quantization, and implement parallelism and batching strategies on multi-GPU nodes • Build the pipeline to move newly released open models from weights to optimized, production-grade deployments within days • Choose and shape the serving stack, including TensorRT-LLM, vLLM, SGLang, custom diffusion and video pipelines, and the Sonic Inference Engine runtime • Own reproducible, hardware-aware benchmarking and performance regression systems wired into CI • Partner with infrastructure and hardware engineering on GPU memory, storage and network bottlenecks • Build and lead the Model Performance team, including hiring, mentoring and setting technical standards • Use AI agents for automated kernel search and tuning, benchmark generation, profiling triage and documentation

🎯 Requirements

• 10+ years in GPU, HPC or deep learning systems engineering, including several years optimizing inference performance for production models at scale • Track record of shipping performance improvements and taking state-of-the-art models from reference implementation to the Pareto frontier of latency and cost on real hardware • Deep, current, hands-on CUDA and GPU architecture expertise, including kernel design, memory hierarchy and occupancy, Tensor Cores, fused kernels, CUDA Graphs, NCCL, Nsight Systems and Nsight Compute • Production experience with at least one major inference engine: TensorRT-LLM, vLLM, SGLang or similar • Experience with continuous batching, paged KV cache, speculative decoding, prefill/decode disaggregation, quantization, tensor parallelism, pipeline parallelism and expert parallelism • Fluent in Python and C++ • Comfortable with PyTorch internals, torch.compile and Triton • Experience leading engineers, building or running a performance team, hiring, setting technical direction and remaining hands-on • Daily use of AI for kernel search, profiling analysis, benchmarking, documentation and code review • Ability to own outcomes in a fast-moving, ambiguous environment and be accountable for latency, cost and quality across hundreds of thousands of models

🏖️ Benefits

• Meaningful stock options • Remote-first work arrangement • In-person meetings twice a year • Core hours for collaboration and calls; flexible schedule outside core hours • Generous paid time off, including vacation, sick days and public holidays • Paid maternity, paternity and caregiver leave • Twice-yearly company retreats • GPU budget and company backing for inference performance work

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