Machine Learning PhD Student Intern - Agentic AI

Summary

As a member of our ML group, you will design, prototype, and ship agentic GenAI systems used by chip design engineers to accelerate their day-to-day workflows. We are looking for a machine learning engineer who is energized by taking LLMs from "promising demo" to "depended-on tool": designing the agent loops, retrieval pipelines, and evaluation frameworks that make this transition real. Success in this role means agents that meaningfully shift how our customer teams work, backed by measurements you trust.

Minimum Qualifications

MSc, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related fields. Strong programming skills in Python and experience with modern ML and deep learning frameworks (e.g., PyTorch). Hands-on experience with agentic frameworks, tool use, and data pipelines. Strong evaluation mindset: comfortable designing golden sets, metrics, and telemetry to drive iteration. Excellent communication and collaboration skills, with the ability to align with subject-matter experts and translate their domain knowledge into agent behavior.

Preferred Qualifications

Hands-on experience with stateful, multi-turn agentic frameworks and complex execution flows. Experience with prompt engineering and optimization techniques. Experience with model adaptation (e.g., fine-tuning, distillation, preference optimization). Publications at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, or similar). Familiarity with chip design or EDA environments.

Description

You will join a growing team of ML and software engineers building GenAI solutions for chip design teams. In this role, you will: Design and build agentic systems featuring multi-agent coordination, long-horizon reasoning, tool use, and planning frameworks. Develop end-to-end solutions, including domain-specific agents and high-performance retrieval pipelines spanning diverse data sources. Partner with subject-matter experts on the customer teams to translate their workflows and rules of thumb into concrete agent assignments, prompts, tools, and guidelines. Build the evaluation backbone for our agents: define success measures, curate offline golden sets, and instrument online telemetry to power feedback loops that drive fast, safe iteration with real cost and quality control. Drive prompt engineering, model selection, and lightweight model adaptation as the workloads demand. Collaborate closely with software engineers on the team to productionize your work, and with researchers and customers to shape what we build next. Stay current with the rapidly evolving agentic and LLM landscape and bring promising techniques into our products with good engineering judgment.