Applied AI NCG (Non Silicon)
- Compensation
- Not disclosed
- Location
- San Jose, CA
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Nice to have
- Experience building or evaluating LLM- or GenAI-powered applications, agents, copilots, retrieval or knowledge experiences, or workflow automation.
- Experience taking a prototype through deployment, end-user testing, feedback, maintenance, or governance.
- Familiarity with cloud development environments, data pipelines, observability, dashboards, logs, or metrics-driven improvement.
- Experience integrating software with enterprise tools, internal knowledge systems, APIs, or business workflows.
- Experience defining evaluation approaches, analyzing failure modes, improving output quality, or measuring adoption and efficiency.
- Ability to communicate technical tradeoffs and make thoughtful recommendations in an evolving problem space.
Full description
Applied AI NCG (Non Silicon)
Location: San Jose, California, United States
Employment type: New College Graduate (NCG)
About the role
Astera Labs is seeking a new college graduate to join the Applied AI team and help build practical AI-powered solutions for non-silicon workflows. You will work at the intersection of machine learning, generative AI, full-stack software, data, and agentic systems to turn real user and business needs into reliable tools that can be tested, deployed, adopted, and improved.
This role is suited for an early-career engineer who enjoys working across the stack, learning unfamiliar systems, and taking ownership from problem definition through implementation, user testing, deployment, and ongoing improvement.
What you will do
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Build, prototype, test, deploy, and maintain AI/ML applications, copilots, agents, and workflow automations for internal users and cross-functional teams.
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Translate stakeholder needs into clear requirements, system designs, workflow definitions, evaluation plans, and measurable success criteria.
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Develop software and integrations that connect models with approved knowledge sources, enterprise systems, data, and user workflows.
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Apply full-stack and ML/GenAI programming skills to create usable experiences, automation, and decision support for non-silicon use cases.
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Design practical agent and system behaviors, including tool use, handoffs, exception paths, permissions, and governance considerations.
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Evaluate prototypes and production behavior through functional testing, end-user testing, metrics, logs, and structured user feedback.
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Investigate failures, diagnose quality or reliability issues, and iterate on prompts, workflows, code, data, and system configuration.
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Help improve data quality, observability, reporting, and operational processes that make AI solutions more useful and maintainable.
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Collaborate with engineering, IT, HR, Finance, Operations, and other stakeholders to understand workflows and deliver solutions that create measurable efficiency.
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Communicate progress, risks, tradeoffs, and decisions clearly; document setup, assumptions, testing, maintenance, and future opportunities.
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Take ownership of well-scoped projects and present outcomes, quantitative results, and recommendations to technical and cross-functional audiences.