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The Staff Forward Deployed Engineer drives AI adoption for strategic customers by solving complex cloud infrastructure challenges, developing scalable assets, and influencing product roadmaps through collaboration and technical expertise.

The Staff Forward Deployed Engineer will work with AI-Native customers to solve cloud infrastructure challenges, build scalable tools, optimize AI workloads, and influence product development by embedding with clients and establishing best practices.

Manage key accounts by providing technical consultation, architectural design, and proactive engagement to drive customer growth on the DigitalOcean platform.

Responsible for deploying and maintaining data center infrastructure, troubleshooting hardware and network issues, managing inventory, and setting up data centers.

The Manager, Customer Success Engineering at DigitalOcean leads a team supporting strategic customers in cloud and AI/ML workloads, focusing on operational excellence and customer satisfaction.

Lead the Use & Accounts engineering team, defining technical strategy, enhancing customer console experience, ensuring system stability, and managing team performance.

Lead architecture and low-level optimizations for LLM inference to maximize throughput and minimize latency. Profile and optimize GPU kernels, implement quantization and parallelization strategies (FP8/INT8/FP4), tune libraries (AITER, Triton, CUDA, ROCm), and mentor engineers while translating hardware limits into shippable platform features.

Lead design and implementation of scalable, multi-tenant serverless inference services focused on throughput, GPU utilization, resiliency, observability, and operational tooling. Partner with platform and GPU teams, debug production performance issues, mentor engineers, drive incident response, and create reusable patterns, standards, and automation for inference workloads.

Lead benchmarking and performance optimization for AI inference at GPU/kernel levels. Solve memory bandwidth and compute bottlenecks, implement quantization and parallelization (multi-node), advise on hardware/software stacks, mentor engineers, and collaborate to ship high-performance inference features.

Lead architecture and low-level optimizations for high-performance AI inference. Benchmark and tune GPU kernels, memory and precision management, parallelize across multi-node GPU clusters, implement quantization (FP8/INT8/FP4), advise on hardware/software stacks (CUDA/ROCm/Triton/TensorRT), mentor engineers, and collaborate to turn hardware limits into shippable inference products.