Principal Engineer, Model Optimizations

The role

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.

All jobs

Principal Engineer, Model Optimizations

DigitalOcean, Seattle +1

Posted pay

$250K–$312K

a year

Compared with similar jobs

This base pay is above the median for principal software engineering jobs in other US places.

Median: $228K a year
Other jobs in the group: 236

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: Seattle +1
Work type: Not given
Level: Principal
Experience: 12+ years
Skills in the job text: PyTorch
vLLM
Posted: Oct 8, 2026
Last check: 8 h ago
Job type: Software engineering, Inference & ML systems, ML / AI engineering

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DigitalOcean

Open tech jobs: 54
With posted pay: 69%
Median posted base: $175K–$215K

About DigitalOcean: funding, hiring and pay

DigitalOcean

About DigitalOcean: funding, hiring and pay

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The role

We are looking for a Principal Engineer to own how models are optimized for serving across our entire accelerator fleet—NVIDIA and AMD alike.

What you will do

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

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.

Principal Engineer, Model Optimizations

The role

Principal Engineer, Model Optimizations

The role

What you will do

What You'll Add to DigitalOcean

Nice to have

Why You’ll Like Working for DigitalOcean

Pay and benefits

About DigitalOcean

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