Principal Software Engineer, Inference
This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.
Who We Are
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world.
Job Description
HPE's Private Cloud AI organization is seeking a Principal Software Engineer to lead the model runtime within HPE AI Essentials, the inference platform used by enterprises to operate large language models on infrastructure they own, including air-gapped and sovereign environments. The principal engineering challenge in this domain is not model deployment but sustained execution efficiency: achieving low tail latency and high GPU utilization on customer-owned hardware of varying generation and configuration. In this role you will define the architecture of that runtime – engine integration, batching, KV cache management, and distributed execution – together with the Kubernetes orchestration layer that supports it. The primary work location is as listed, but could be any other HPE site location in the US; however, remote work options will be considered.
Responsibilities
•Define and own the technical direction of the LLM serving deployment, including engine integration, continuous batching, KV cache management and reuse, and quantized execution
•Partner with inference performance engineering teams, with accountability for time-to-first-token, inter-token latency, throughput per GPU, and P95/P99 tail latency
•Define distributed inferencing strategy, including disaggregated prefill/decode, tensor and pipeline parallelism, 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 determine whether each runtime is adopted, developed in-house, or declined
•Define the orchestration layer supporting the runtime, including model admission, GPU scheduling and partitioning, cache-aware request routing, and autoscaling
•Mentor engineers, lead design and architecture reviews, and present technical direction to business unit and executive audiences
Knowledge and Skills
Required
•Production experience with LLM inference engines such as vLLM, SGLang, TensorRT-LLM, TGI, or NVIDIA NIM, including modification of engine internals
•Comprehensive understanding of inference internals, including continuous batching, paged attention, KV cache reuse and prefix caching, chunked prefill, quantization, and speculative decoding
•Tensor and pipeline parallelism, NCCL collective operations, and the GPU memory hierarchy and interconnect characteristics that govern them
•Expert level 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
•Experience with debugging/profiling multi-tier application workloads such as RAG, Agents, etc
•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 12 years of experience in Software Engineering, including +1 years working directly on LLM inference runtimes or production model serving
•Degree in Computer Science or related field
What We Can Offer You
•Health & Wellbeing: a comprehensive suite of benefits that supports physical, financial and emotional wellbeing
•Personal & Professional Development: career-focused programs
•Unconditional Inclusion: an inclusive culture that celebrates individual uniqueness and flexibility to manage work and personal needs
Salary
The expected salary/wage range for this position (actual offer may vary based on geographic location, work experience, education/training, and/or skill level):
•United States of America: Annual Salary USD 160,000 – 303,000 in Colorado
•USD 152,000 – 349,000 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 myhperewards.com/main/new-hire-enrollment.html.
The estimated job application period closure is January 11, 2027.
