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DigitalOcean

Runs cloud infrastructure for apps and AI

Staff Engineer - Inference Optimizations

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSeattle, WA, USA

About the Job

Requirements

  • At least 5 years of experience in high-performance computing or AI infrastructure, with a track record of solving compute-utilization and memory-bandwidth bottlenecks.
  • Deep familiarity with the generative AI landscape, including large language models, vision-language models, and large multimodal models, and their architectural requirements.
  • Hands-on experience with attention-layer optimizations and parallelization strategies across distributed GPU environments.
  • Comprehensive understanding of NVIDIA and AMD GPU architectures and their software ecosystems, including CUDA and ROCm.
  • Extensive experience integrating, building with, and contributing to open-source software projects.
  • System design skills related to low-level GPU programming, optimization, memory-access patterns, and parallel execution.
  • Experience acting as a technical lead and driving design and delivery through cross-functional alignment and expert-level delegation.
  • Deep understanding of GPU architectures, including streaming multiprocessors, warp scheduling, and tensor cores.
  • Expert-level proficiency in Triton or CUDA; experience contributing to the Triton compiler or writing custom CUDA kernels for a major large language model is specifically sought.

Responsibilities

  • Lead technical strategy for benchmarking and performance optimization at the inference-engine and GPU-kernel layers to maximize infrastructure performance.
  • Engineer solutions for complex performance issues, including attention-layer optimization, memory and precision management, and advanced parallelization across multi-node GPU clusters.
  • Implement optimization techniques, such as improving batch-size performance with AMD’s AITER library for AMD MI355X by tuning composable-kernel or assembly options for FP8 and BF16; identifying kernel-fusion opportunities for GLM-5 Transformer layers such as FlashAttention and RMS Norm; and tuning expert-gateway router kernels for mixture-of-experts models.
  • Serve as a subject-matter expert on modern NVIDIA and AMD GPU families and their software stacks, including CUDA, ROCm, TensorRT, and OpenAI Triton; advise on hardware procurement and software integration.
  • Develop and deploy quantization techniques, including FP8, INT8, and experimental FP4, to increase throughput while maintaining accuracy.
  • Provide technical mentorship through high-quality code and design reviews, raising the team’s technical standards without direct administrative management.
  • Partner with Product Management and technical program managers to translate hardware limits into product features.
  • Participate in GPU infrastructure and model-performance-optimization communities and contribute to and integrate open-source AI.

About the Company

DigitalOcean

Runs cloud infrastructure for apps and AI

DigitalOcean provides cloud infrastructure for developers and businesses to build, host and scale applications and AI services. Teams rent virtual machines called Droplets, store data and run managed databases or Kubernetes clusters through a web console and APIs. Its AI services supply GPU computing, model inference and managed environments where software agents use tools and retain information between sessions. Customers pay for the resources they use, with subscriptions and managed services helping teams run applications without maintaining all the underlying infrastructure themselves. DigitalOcean was founded in 2012 and is headquartered in Broomfield, Colorado.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Broomfield, CO
Founded
2012

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