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About This Role
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, realworld 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.
Essential Duties and 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.
Education and Experience:
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.
With more than 3000 employees worldwide, we offer challenging and rewarding work experiences, generous employee benefits and a strong company culture. If you are looking for a global publicly traded company that provides you with international experience and a challenging work environment, then Cohu is your choice. Connect with Cohu… Connect with your future…
Compensation: The estimated base pay range for this position is $125,000 - $175,000 per year, depending on qualifications, experience, education, and other job-related factors. This range represents Cohu’s good faith estimate of what it reasonably expects to pay for this role upon hire. This role may also be eligible for other compensation programs as determined by company policy. Featured benefits Medical, dental & vision insurance 401(k) with company matching contributions Employee Stock Purchase Plan Tuition assistance Disability & life insurance Profit Sharing
Cohu firmly supports the U.S. national and various state and local policies of equal employment opportunity which are designed to provide equality of employment and advancement opportunities to every individual without regard to unlawful considerations of race, color, religion, national origin, citizenship status, ancestry, gender, gender identity or gender expression, age, marital status, sexual orientation, disability, medical conditions, pregnancy, genetic information, military or veteran status or any other legally protected category. In addition, reasonable accommodations are available to qualified disabled individuals, upon request. Globally, Cohu is committed to full compliance with all applicable laws and regulations governing employment, in the U.S. and in all other locations around the world where we have operations.
Pay: $125,000.00 - $175,000.00 per year
Work Location: Remote
Salary Context
This $125K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Xcerra, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $125K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Xcerra AI Hiring
Xcerra has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Seattle, WA, US. Compensation range: $175K - $225K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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