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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.

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 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 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 delivery of high-scale, resilient distributed inference data plane services. Architect and optimize inference hosting (tensor/data parallelism, KV-cache routing, prefill/decode disaggregation), contribute upstream open-source projects, mentor engineers, and maintain operational excellence and SLOs for a multi-tenant AI inference cloud.

Lead benchmarking and low-level performance optimization for GPU-based inference. Solve memory, precision, and parallelization bottlenecks, tune kernels (CUDA/Triton/ROCm/AMD AITER), implement quantization (FP8/INT8/FP4), advise on hardware/software integration, mentor engineers, and translate hardware limits into shippable features for high-throughput, low-latency inference fleets.

Lead design and delivery of high-scale, resilient distributed inference data-plane services for LLM hosting. Optimize performance (tensor/data parallelism, KV-cache routing, prefill/decode disaggregation), collaborate cross-functionally, contribute upstream to open-source inference projects, mentor engineers, and operate production services with strong SLOs and observability.

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.