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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer, Inference - **Company:** Hewlett-Packard Enterprise - **Location:** Spring, TX, United States (Remote available) - **Salary:** $160,000.0 - $303,000.0 - **Contract:** Permanent contract - **Skills:** Multitier Architecture, Artificial Intelligence, C++ (Programming Language), Profiling, Nvidia CUDA, Computer Programming, Software Debugging, InfiniBand, Python (Programming Language), Remote Direct Memory Access, Software Engineering, Private Cloud Environment, Enterprise Software Applications, Large Language Models, Kubernetes, Information Technology, TensorRT, Nim (Programming Language) - **Published:** September 17, 2026 - **Apply:** https://hpe.wd5.myworkdayjobs.com/Jobsathpe/job/Spring-Texas-United-States-of-America/Principal-Software-Engineer--Inference_1211357-2 ## About the Role · 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, · 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 ## Description This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office., 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., · 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 ## Related Videos - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [The Gashlycrumb Tinies of AI Networking You Must Know (or Languish!)](https://www.wearedevelopers.com/videos/2067-the-gashlycrumb-tinies-of-ai-networking-you-must-know-or-languish) - [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire) - [Enhancing Workload Security in Kubernetes](https://www.wearedevelopers.com/videos/356-enhancing-workload-security-in-kubernetes) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Got AI ideas but no money? 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