Job description

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. We are looking for a Principal Engineer to own how models are optimized for serving across our entire accelerator fleet—NVIDIA and AMD alike. DigitalOcean is the Inference Cloud. The Inference Platform team runs frontier open models in production on a heterogeneous GPU fleet, and the gap between a model that merely runs and a model that runs well is measured in millions of dollars of GPU time and in whether a customer's SLO is met. Closing that gap is this role. You will own the model optimization discipline end to end: quantization strategy and its accuracy budget, kernel selection and authoring, attention and MoE execution, speculative decoding, and the parallelism layout that ties them together. You'll do it across model architectures that change every few months and across two vendor stacks with genuinely different performance characteristics—CUDA/Hopper/Blackwell on one side, ROCm/MI300-class and beyond on the other. The hard part is not making one model fast on one GPU. It's building the methodology, tooling, and upstream relationships that make every model fast on every GPU we buy, and doing it fast enough to launch a new model the week it drops. What You'll Be Doing: Setting the technical strategy for model optimization across the fleet: which techniques we invest in, which we consume upstream, and how we decide Owning quantization end to end—FP8, FP4/MXFP4, INT8, weight-only schemes such as AWQ and GPTQ—including calibration methodology, the accuracy budget we're willing to spend, and the eval gates that enforce it Driving performance for modern architectures at the execution level: MoE routing and fused expert kernels, MLA and GQA attention variants, long-context and sliding-window attention, and the memory-movement patterns each implies Leading speculative decoding work (draft models, EAGLE/Medusa-class methods, n-gram and lookahead approaches) and the acceptance-rate tuning that determines whether it actually pays Writing and tuning kernels where upstream falls short—CUDA, Triton, CUTLASS, and their ROCm counterparts (HIP, Composable Kernel, hipBLASLt, AITER)—and knowing when not to Establishing how we choose parallelism layouts (TP, PP, EP, attention DP) per model, per GPU family, per traffic shape, and encoding that into something repeatable rather than tribal knowledge Building the benchmarking and regression infrastructure that makes optimization claims trustworthy: agent-shaped and long-context traffic distributions, TTFT/ITL/throughput measurement, and accuracy validation that runs before anything ships Making AMD a genuinely first-class target rather than a port, and driving the upstream work in vLLM, SGLang, and TensorRT-LLM required to get there Partnering with NVIDIA and AMD engineering teams on pre-silicon enablement, early-access hardware, and roadmap feedback Setting technical direction, mentoring senior and staff engineers, and representing DigitalOcean in upstream communities, at conferences, and in customer technical deep dives What You'll Add to DigitalOcean: 12+ years in performance-critical systems, with substantial recent experience optimizing LLM inference in production Deep understanding of GPU architecture and the inference performance model: memory bandwidth vs. compute bounds, arithmetic intensity, kernel launch and scheduling overhead, and where prefill and decode differ Hands-on kernel-level experience on at least one vendor stack and the demonstrated ability and appetite to work across both—CUDA/CUTLASS/Triton and ROCm/HIP/CK Practical quantization expertise, including the judgment to know when a technique that benchmarks well will fail a customer's accuracy bar Familiarity with the internals of at least one major serving engine (vLLM, SGLang, TensorRT-LLM), sufficient to land non-trivial changes upstream Strong Python and C++/CUDA skills, and rigor in profiling with Nsight, rocprof, and equivalent tooling A measurement-first disposition: claims backed by reproducible benchmarks, with an understanding of how easily inference benchmarks mislead Excellent written and verbal communication, and experience leading cross-functional efforts spanning infrastructure, product, and customers Bonus: Meaningful upstream contributions to vLLM, SGLang, TensorRT-LLM, PyTorch, Triton, or ROCm Experience bringing up a new accelerator family for production inference, including the unglamorous parts—numerics differences, missing kernels, immature libraries Experience with disaggregated prefill/decode serving and its interaction with parallelism and quantization choices Track record of same-week or day-zero enablement for newly released frontier open models Publications or patents in efficient inference, quantization, or GPU kernel design Compensation Range: $249,600 - $312,000 Non-sales roles : In addition to base salary, this role is eligible to participate in DigitalOcean’s annual bonus plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. Bonus amounts depend on company and individual performance. Sales roles (sales compensation eligible) : In addition to base salary, this role may be eligible to earn incentive compensation under the applicable sales compensation plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. *This is a hybrid role JR: 2026-8158 #LI-Hybrid Why You’ll Like Working for DigitalOcean We innovate with purpose. You’ll be a part of a cutting-edge technology company with a high-growth trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As part of our team, you’ll embody our "Shark" mindset: bold, ambitious, and scrappy. You'll lead with a bias for action and take true ownership of our products, customer success, and team culture. We prioritize career development. At DigitalOcean, you’ll do the best work of your career. You will join a community of exceptional talent, collaborating closely with some of the brightest and most interesting people in the industry. We are a high-performance and fast-paced organization that will always challenge you to think big. Our talent development team prioritizes your professional development and provides resources to support your growth. We provide employees with reimbursement for relevant conferences, training, and education, subject to company policy. We care about your well-being. We take a comprehensive approach to our total rewards packages because we know different things drive different people. Our benefits are designed to support you and may include flexible time off, local team meetups, and mental health and well being support. Our benefits philosophy is consistent globally, but specific benefits and eligibility vary by location to reflect local needs and regulations. We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. Eligible employees may also receive equity compensation, which may include a new hire equity grant and, in participating countries, the opportunity to participate in our Employee Stock Purchase Program. Equity awards are subject to approval and the terms of the applicable plan. Non-sales roles : In addition to base salary, this role is eligible to participate in DigitalOcean’s annual bonus plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. Bonus amounts depend on company and individual performance. Sales roles (sales compensation eligible) : In addition to base salary, this role may be eligible to earn incentive compensation under the applicable sales compensation plan and may be eligible for a new hire equity grant, subject to plan terms and local eligibility. DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, sex, disability, protected veteran status, or any other characteristic protected by applicable law. Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.

Requirements

Must-haves

Setting the technical strategy for model optimization across the fleet: which techniques we invest in, which we consume upstream, and how we decide
Owning quantization end to end—FP8, FP4/MXFP4, INT8, weight-only schemes such as AWQ and GPTQ—including calibration methodology, the accuracy budget we're willing to spend, and the eval gates that enforce it
Leading speculative decoding work (draft models, EAGLE/Medusa-class methods, n-gram and lookahead approaches) and the acceptance-rate tuning that determines whether it actually pays
Writing and tuning kernels where upstream falls short—CUDA, Triton, CUTLASS, and their ROCm counterparts (HIP, Composable Kernel, hipBLASLt, AITER)—and knowing when not to
Establishing how we choose parallelism layouts (TP, PP, EP, attention DP) per model, per GPU family, per traffic shape, and encoding that into something repeatable rather than tribal knowledge
Making AMD a genuinely first-class target rather than a port, and driving the upstream work in vLLM, SGLang, and TensorRT-LLM required to get there
Partnering with NVIDIA and AMD engineering teams on pre-silicon enablement, early-access hardware, and roadmap feedback
Setting technical direction, mentoring senior and staff engineers, and representing DigitalOcean in upstream communities, at conferences, and in customer technical deep dives
12+ years in performance-critical systems, with substantial recent experience optimizing LLM inference in production
Deep understanding of GPU architecture and the inference performance model: memory bandwidth vs. compute bounds, arithmetic intensity, kernel launch and scheduling overhead, and where prefill and decode differ
Hands-on kernel-level experience on at least one vendor stack and the demonstrated ability and appetite to work across both—CUDA/CUTLASS/Triton and ROCm/HIP/CK
Practical quantization expertise, including the judgment to know when a technique that benchmarks well will fail a customer's accuracy bar
Familiarity with the internals of at least one major serving engine (vLLM, SGLang, TensorRT-LLM), sufficient to land non-trivial changes upstream
Strong Python and C++/CUDA skills, and rigor in profiling with Nsight, rocprof, and equivalent tooling
A measurement-first disposition: claims backed by reproducible benchmarks, with an understanding of how easily inference benchmarks mislead
Excellent written and verbal communication, and experience leading cross-functional efforts spanning infrastructure, product, and customers
$249,600 - $312,000
This is a hybrid role
#LI-Hybrid Why You’ll Like Working for DigitalOcean

Nice-to-haves

Meaningful upstream contributions to vLLM, SGLang, TensorRT-LLM, PyTorch, Triton, or ROCm
Experience bringing up a new accelerator family for production inference, including the unglamorous parts—numerics differences, missing kernels, immature libraries
Experience with disaggregated prefill/decode serving and its interaction with parallelism and quantization choices
Track record of same-week or day-zero enablement for newly released frontier open models
Publications or patents in efficient inference, quantization, or GPU kernel design

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmpythonai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Pto

Professional Development