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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 and manage the Security Products engineering team, driving technical roadmap, ensuring quality, and fostering team growth. Oversee product delivery and security practices.

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.

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 implementation of high-scale, resilient AI inference data plane services. Architect distributed LLM serving systems, optimize performance (tensor/data parallelism, KV-cache routing), build on Kubernetes-native inference frameworks, contribute to open-source projects, mentor engineers, and operate production services with strong observability and SLOs.

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.