Responsibilities
Design, implement, and own major components of the LLM serving deployment, including engine integration, continuous batching, KV cache management and reuse, and quantized execution
Partner with inference engineering teams and contribute to improving time-to-first-token, inter-token latency, throughput per GPU, and P95/P99 tail latency
Build and operate distributed execution capabilities, including disaggregated prefill/decode, tensor and pipeline parallelism, and KV cache offload across GPU memory, host memory, and RDMA-attached storage
Evaluate emerging runtimes, quantization schemes, speculative decoding, and mixture-of-experts serving, and make well-supported recommendations on adoption
Contribute to the orchestration layer supporting the runtime, including model admission, GPU scheduling and partitioning, cache-aware request routing, and autoscaling
Triage and resolve customer issues end-to-end, identifying root causes and improving systems and processes to prevent recurrence
Provide insightful code and design reviews, mentor team members, and lead by example on engineering practices within the team
Knowledge and Skills
Required
Familiar with LLM inference engines such as vLLM, SGLang, TensorRT-LLM, TGI, or NVIDIA NIM, including modification of engine internals
Strong understanding of inference internals, including continuous batching, paged attention, KV cache reuse and prefix caching, chunked prefill, quantization, and speculative decoding
Working knowledge of tensor and pipeline parallelism, NCCL collective operations, and the GPU memory hierarchy and interconnect characteristics that govern them
Advanced proficiency in Kubernetes platform architectures, including operators, custom resources, controllers, and scheduling
Strong programming proficiency in Go and Python, with the ability to read, debug, and profile C++/CUDA using tools such as Nsight
Familiar with debugging/profiling multi-tier application workloads such as RAG, Agents
Excellent analytical, debugging, and problem-solving abilities
Preferred
Upstream contribution to vLLM, SGLang, TensorRT-LLM, llm-d, LMCache, or KServe
Disaggregated prefill/decode serving, or KV cache offload and reuse at scale
RDMA, GPUDirect Storage, InfiniBand, or RoCE
MIG, fractional GPU allocation, and multi-tenant GPU isolation
On-premises, air-gapped, or regulated enterprise software delivery
Experience and Education
Minimum of 8 years of experience in Software Engineering, including 1-2+ years working directly on LLM inference runtimes or production model serving
Degree in Computer Science or related field
Accessibility
HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here.
Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.
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Job
- Engineering
- Job Level:
- TCP_04
- "The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level.
- United States of America: Annual Salary USD 144,000 - 273,000 in Colorado // 137,000 - 315,000 in North Carolina & Texas
- The listed salary range reflects base salary. Variable incentives may also be offered."
Information about employee benefits offered in the US can be found at https://myhperewards.com/main/new-hire-enrollment.html
The estimated job application period closure is December 30 2027; this timeline is provided for transparency and internal planning purposes.
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