HPE Labs - AI/ML Engineer (Technical Lead)

Location: Onsite at the HPE office in Milpitas, California, United States

Base salary: USD 178,000–361,000 annual salary in California; variable incentives may also be offered.

Company

HPE Labs’ AI Enablement team turns AI research and engineering ideas into dependable, governed, reusable capabilities for HPE business units and customers.

What you will do

• Lead selected high-impact AI use cases from problem framing and solution design through production deployment and transition to operational ownership.
• Design scalable AI architectures with strong performance, reliability, security, supportability, evaluation, guardrails, monitoring, documentation, and measurable outcomes.
• Develop reference architectures, tooling, and repeatable patterns that accelerate future AI use cases.
• Partner with business units, product, engineering, data, security, and HPE Labs to translate requirements and trends into AI products and systems improvements.
• Provide technical leadership through architecture and design reviews, communicate tradeoffs to technical and executive stakeholders, and secure adoption and operational ownership.
• Mentor engineers, promote engineering best practices, and contribute to intake, prioritization, governance, and delivery practices.

Requirements

• Typically 10+ years of relevant experience in applied AI/ML engineering or software engineering, including hands-on delivery of AI/ML systems into production.
• Ability to independently take AI/ML solutions from concept to production, including data pipelines, deployment, evaluation, and operationalization.
• Experience shipping an LLM or agentic system used by real users, with ownership of evaluation, reliability, and post-launch operations.
• Experience building and scaling enterprise agentic AI solutions in complex, production-grade environments.
• Current practical depth in supervised and unsupervised learning, deep learning, NLP, LLMs, generative AI, agentic systems, statistical modeling, LLMOps or MLOps, evaluation, and production model lifecycle management.
• Solid systems and software engineering fundamentals and experience with cloud-native and/or hybrid deployment environments.
• Working knowledge of responsible AI and governance, including evaluation, guardrails, security by design, and data privacy.
• Bachelor’s or master’s degree in computer science, computer engineering, data science, or a related field, or equivalent practical experience.
• Ability to lead technical work and influence across teams without formal authority; strong strategic, communication, stakeholder-management, judgment, and problem-solving skills.
• Track record of mentoring and developing less-experienced engineers.

Culture & Benefits

• Collaborative, high-standards culture focused on delivering dependable production systems rather than prototypes.
• Emphasis on intellectual honesty, psychological safety, direct feedback, curiosity, humility, and shared success.
• Health, financial, and emotional wellbeing benefits for employees and their loved ones.
• Professional development programs supporting growth as a subject-matter expert or movement to another division.
• Inclusive workplace with accommodations available for applicants with disabilities.