Job description

Senior 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. 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 Senior Software Engineer to build and evolve 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 core 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 design and implement key components 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 · 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. What We Can Offer You: Health & Wellbeing We 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 Development We 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 Inclusion We 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. #unitedstates 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 myhperewards.com 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 Alert We 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.

Requirements

Must-haves

Senior 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.
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

Nice-to-haves

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
Degree in Computer Science or related field
Accessibility
Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.

Application prep

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

Keywords to mirror

aikubernetesllmgopythonaccessibility

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision

Professional Development