Principal Software Engineer, Inference

Principal Software Engineer, InferenceThis 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. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.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 audiencesKnowledge and SkillsRequired· 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 abilitiesPreferred· 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 deliveryExperience 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 fieldAccessibilityHPE 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.What We Can Offer You:Health & WellbeingWe strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.Personal & Professional DevelopmentWe also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.Unconditional InclusionWe are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.Let's Stay Connected:Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE. #unitedstatesJob:EngineeringJob Level:TCP_05"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 160,000 - 303,000 in Colorado // 152,000 - 349,000 in North Carolina & TexasThe listed salary range reflects base salary. Variable incentives may also be offered."Information about employee benefits offered in the US can be found at The estimated job application period closure is December 30 2027; this timeline is provided for transparency and internal planning purposes.HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.Recruitment Fraud AlertWe have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications.All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual’s own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately.SummaryLocation: Spring, Texas, United States of America; Durham, North Carolina, United States of America; Ft. Collins, Colorado, United States of AmericaType: Full time