Job Description

This role offers the opportunity to work in a start-up-like incubation environment within HP’s PC Design team — moving quickly, solving ambiguous product challenges, and helping build new products that integrate edge AI into next-generation PC experiences. You will work closely with customers and cross-functional partners to understand real-world needs, incorporate feedback into product development, and help translate early concepts into scalable, production-quality software.

Ideal candidates should have demonstrated excellence e.g. successful shipped new products/features at scale, significant contributions to important open-source projects - in two or more of the following areas:

• Large language models,

• Computer vision models,

• Speech recognition and synthesis,

• Audio-video multimodal models

• Human computer intelligent interactions

• Deep neural networks

• Model size reduction

• Synthetic photorealistic image data generation

• Robotics

• Embedded systems

• Cloud computing

• AI Agents

• Other relevant technologies (do tell us about them!)

This role requires strong technical judgment, practical problem-solving skills, and the ability to build reliable software in a fast-moving product development environment.

You will collaborate closely with cross-functional teams across software architecture, UX, product management, hardware engineering, security, manageability, validation, DevOps, systems engineering, and AI/ML engineering to deliver scalable, high-quality software capabilities.

Key Responsibilities

• Design, develop, and maintain full-stack software features across frontend applications, backend services, local application components, cloud-connected workflows AI-enabled platform capabilities.

• Integrate, evaluate, and deploy machine learning models — including LLMs, vision models, and audio models — into production software, covering data preprocessing, inference pipelines, evaluation, and monitoring.

• Work with audio-video and multimodal models to enable capabilities such as meeting understanding, video summarization, audio-visual speaker attribution, and cross-modal search.

• Fine-tune and adapt pre-trained models for product-specific use cases, and define the datasets, benchmarks, and quality metrics used to measure and improve them.

• Design and implement agentic and retrieval-augmented (RAG) workflows that combine LLMs, tool use, and enterprise data sources.

• Design, build, and evaluate agentic AI workflows — task decomposition, planning and reasoning loops, tool and function calling, memory and context management, and multi-agent orchestration — that automate real user workflows end to end.

• Integrate machine learning models with cloud-based platforms and/or embedded systems for deployment in production environments.

• Define guardrails, evaluation harnesses, and observability for agentic systems, including failure handling, human-in-the-loop checkpoints, cost and latency budgets, and reliability metrics.

• Develop automated tests and contribute to CI/CD, build pipelines, code signing, packaging, and release readiness.

• Stay updated with the latest advancements in machine learning, computer vision, robotics, and related fields to drive innovation within the organization.

• Mentor junior team members and actively participate in knowledge sharing activities.

Education and Experience

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, Machine Learning, Mathematics, or equivalent practical experience.

• 4-8 years of full-stack software development experience across frontend, backend, and application-level development.

• Hands-on experience building, fine-tuning, or deploying machine learning models in production systems

Knowledge and Skills

• Experience building modern frontend applications using technologies such as Angular, React, TypeScript, JavaScript, HTML, and CSS.

• Backend development experience with one or more languages such as C++, Rust, Python, C#, Node.js, Go, or similar.

• Experience integrating AI services, including LLMs and modern AI APIs.

• Hands-on machine learning experience with frameworks such as PyTorch, TensorFlow / TensorFlow Lite, ONNX Runtime, or Hugging Face Transformers, including model inference, fine-tuning, and evaluation.

• Experience designing multi-agent and long-running agentic pipelines with state management, retries, deterministic recovery, and evaluation of end-to-end task success.

• Experience with LLMs, Vision Language, and speech models

• Understanding of deep neural networks and model optimization techniques such as quantization, pruning, distillation, and model size reduction for edge and on-device deployment.

• Experience deploying and serving models in cloud and edge environments (AWS, Azure, or Google Cloud, containerized inference, GPU/NPU acceleration).

• Strong Python skills, with practical experience in data preprocessing, dataset creation, and model evaluation metrics.

• Experience developing Windows desktop applications, APIs, and cloud-connected software.

• Strong debugging, performance optimization, and software troubleshooting skills.

• Experience with Git, CI/CD pipelines, automated testing, and modern software development practices.

• Strong written and verbal communication skills with the ability to work effectively across global, cross-functional engineering teams.

Preferred

• Experience with Tauri, WebView, WebRTC, PowerShell, COM, or native Windows development.

• Experience with Microsoft Graph, Azure, Microsoft Intune, ADMX/Group Policy, or enterprise device management.

• Experience building and deploying real-time and streaming audio pipelines — audio capture, resampling, chunking, buffering, WebSocket/gRPC streaming, and low-latency inference.

• Experience with enterprise manageability solutions (MS Intune, ADMX/Group Policy, HP WXP or similar MDM/UEM platforms)

• Experience building telemetry, diagnostics, resource monitoring, benchmarking, and test automation frameworks.

• Experience with authentication, identity, SSO, and enterprise integrations.

• Experience with LLM application patterns such as RAG, function/tool calling, agentic pipelines, and prompt/context optimization.

• Experience running local or on-device inference on NPUs and accelerators (Windows ML/DirectML, OpenVINO, TensorRT, CoreML, or vendor NPU toolchains).

• Experience with computer vision — object detection, OCR and document digitization, pose estimation, or depth/3D estimation.

• Experience with speech synthesis (TTS), audio enhancement, or noise suppression.