Lead AI Software Engineer

HP IQ•San Francisco, CA

4d•$190,000 - $270,000

About The Position

HP IQ, HP’s AI innovation lab, is building an intelligent ecosystem across HP’s portfolio. The AI team develops solutions that bring AI capabilities to edge devices and integrate them with cloud infrastructure. The Lead Software Engineer will design and develop high-performance, scalable services for AI workloads across edge and cloud environments, and help deliver end-to-end AI-driven solutions in collaboration with AI researchers, infrastructure engineers, and frontend developers.

Requirements

At least 10 years of backend software development experience.
Strong proficiency in Java, C++, or Python.
Proficiency integrating LLMs into multi-agent systems, with a strong understanding of agent orchestration, tool use, and memory/context management.
Comfort using prompts to experiment with LLMs and experience with tool use, task decomposition, and structured outputs.
Experience building distributed systems, microservices, and cloud-native applications.
Strong knowledge of building secure, privacy-aware interfaces between models, devices, and external services.
Familiarity with SQL and NoSQL database management systems and data streaming technologies.
Strong problem-solving skills and ability to work in a fast-paced, agile environment.

Nice To Haves

Experience deploying AI models and integrating them into backend services.
Knowledge of edge computing, networking, and security best practices.
Exposure to cloud-edge hybrid architectures and distributed AI workloads.
Familiarity with on-device AI frameworks such as TensorFlow Lite, ONNX Runtime, or CoreML.

Responsibilities

Design, build, and maintain services that power AI-driven applications, ensuring scalability and performance.
Develop APIs and microservices to integrate cloud-based AI models with edge devices.
Optimize data pipelines and storage solutions for real-time AI inference and processing.
Implement security and privacy best practices for distributed AI systems.
Collaborate with AI researchers, infrastructure engineers, and frontend developers to deliver end-to-end AI-driven solutions.
Build and optimize an agent orchestration runtime supporting tool use, memory management, and multi-step reasoning across LLMs, APIs, and edge-connected systems.
Develop logging, monitoring, and alerting systems to support system reliability.