Researcher- Applied AI and Machine Learning

Cohu•San Diego, CA

11h

About The Position

Cohu, Inc. is building an AI group within Semiconductor Testing to solve and deploy solutions to hard, real-world problems that we encounter. This role will take on problems that do not have clean answers yet, build tooling to measure whether your solutions work, and help field and applications engineers reduce their workflow from weeks to hours. This newly created position in the AI Group is for a research role solving ambiguous, real-world AI problems for semi-conductor test-AI. This position will own end-to-end research and delivery on ambiguous, real-world AI problems in semiconductor test: turn messy inputs vague or customer-specific test terminology, undocumented protocols, thousands of pages of unstructured technical documentation into models, retrieval systems, and evaluation methods that hold up under real use. With no playbook to follow, in this role as much time will be spent defining the problem and its success criteria, as solving it. Examples of this kind of work include mapping inconsistent, customer-specific test names and undocumented protocols into a structured internal test library; converting undocumented manual test procedures into automated test programs; and building a RAGbased retrieval engine that gives engineers cited, source-linked answers from expansive tester documentation instead of making them dig for it themselves.

Requirements

Advanced degree in Computer Science with specialization in ML/AI
Advanced degree in physics, electrical engineering, signal processing, or a related scientific domain preferred.
3–5 years of applied ML/research experience, or equivalent depth from an advanced degree in a quantitative field.
In-depth understanding of the scientific method: ability to design experiments, form hypotheses, and both ask and answer ambiguous questions independently.
Applied experience with modern ML/AI techniques: retrieval-augmented generation and knowledge-graph retrieval, model fine-tuning, and agent frameworks.
Experience designing evaluation methods and benchmarks for AI/ML systems, with the statistical rigor to know when a result is real versus noise.
Strong programming fundamentals and Python fluency, sufficient to build and iterate.
Working familiarity of AI coding agents (Claude Code, Cursor, Codex, Antigravity, or similar) by default, not because a policy directs you to.
Demonstrated ability to scope and solve ambiguous technical problems independently.
Excellent written and verbal communication; must be able to explain research findings and tradeoffs to cross-functional partners who don’t share your technical background.
Semiconductor industry familiarity, particularly in semiconductor testing, ATE, or EDA tools preferred.
Understanding of software engineering fundamentals and best practices.
Evidence of self-led learning into new technical areas outside your formal training.
Exposure to GPU/server hardware and at least one LLM inference or fine-tuning runtime (e.g., vLLM, Hugging Face Trainer/PEFT).
Knowledge of C++ for performance-critical or hardware-adjacent work preferred.

Nice To Haves

Advanced degree in physics, electrical engineering, signal processing, or a related scientific domain
Semiconductor industry familiarity, particularly in semiconductor testing, ATE, or EDA tools
Knowledge of C++ for performance-critical or hardware-adjacent work

Responsibilities

Collaborate with the engineering team and domain experts to evaluate and scope ambiguous problems, turning a vague pain point into a concrete, measurable target.
Learn existing programs, protocols, and domain documentation deeply enough to separate the real constraint from an assumed one.
Design experiments and establish success criteria and evaluation benchmarks aligned with business goals, with the statistical rigor to defend a result.
Develop and fine-tune models, algorithms, and retrieval/knowledge-graph methods to evaluate and improve proposed solutions.
Design and build agent systems and the RAG/knowledge-retrieval pipelines that connect AI reasoning to domain-specific toolsets.
Work with the engineering team to deploy rapid solutions into production, not just research prototypes.
Stay ahead of emerging ML/AI research and tooling so the group isn’t surprised by what competitors or customers adopt next, and bring back what’s actually usable, not just novel.
Explore emerging platform capabilities as the system matures, proposing new project directions rather than only executing assigned ones.