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About This Role
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
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Chips are at the center of today's tech\-driven world. But how we design and verify them has not fundamentally changed in decades, while their complexity and specialization have skyrocketed due to increasing performance demands from AI. We are a dynamic, fast\-moving team of software developers, ML scientists, and research\-minded engineers on a mission to change that.
Operating with the agility of a startup but backed by industry\-leading verification technologies, we are part of the System Verification Group (SVG). Our charter is to develop state\-of\-the\-art EDA software and hardware platforms (including Xcelium, Jasper, Palladium, Protium, and Helium) and supercharge them with cutting\-edge AI, automation, and advanced data\-driven workflows.
About This Role
Cadence Design Systems is the leading provider of design automation tools for electronic and intelligent systems design. The ML / Software Engineer – ChipStack SuperAgent Team will be responsible for designing, implementing, and evaluating AI agents that enhance productivity across the semiconductor design lifecycle. This engineer will contribute to the development of robust agent infrastructure, evaluation systems, and production\-grade AI capabilities integrated within Cadence’s electronic design automation (EDA) ecosystem.
The role focuses on building reliable, scalable agentic systems that operate within complex engineering workflows. The ideal candidate combines strong software engineering fundamentals with practical experience in ML systems and agent infrastructure, enabling deployment of high\-impact AI solutions in production environments.
In this role, you will be part of the ChipStack AI Super Agent team and will operate at the forefront of semiconductor design and AI innovation, utilizing advanced AI tools to architect, design, and validate the next generation of verification methodologies. You will collaborate closely with a highly skilled team of machine learning engineers experienced in training large language models at scale, as well as accomplished software engineers with proven expertise in product development and deployment. You will be working on the world’s first agentic AI platform that autonomously designs and verifies chips with up to 10× productivity gains.
Responsibilities
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- Design and implement scalable infrastructure for AI agents operating within Cadence’s ChipStack SuperAgent ecosystem.
- Build robust evaluation frameworks to measure agent performance, reliability, and alignment with engineering workflows.
- Develop data pipelines, retrieval systems, and context\-engineering strategies to support consistent and grounded agent behavior.
- Contribute to continuous integration, automated testing, and observability systems to ensure production\-quality deployment of AI\-enabled systems.
- Optimize system performance across latency, cost, reliability, and scalability dimensions.
Required Qualifications
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- BS with a minimum of 10 years of experience OR MS with a minimum of 7 years of experience OR PhD with a minimum of 5 years of experience
- Strong software engineering fundamentals, including design, refactoring, debugging, and testing of complex distributed systems. Demonstrated experience building production\-quality systems.
- Understanding of large language models (LLMs) and practical considerations for deploying them in real\-world systems (latency, cost, reliability, monitoring).
- Experience designing evaluation frameworks for AI systems, including benchmarking, regression testing, and failure analysis.
Skills of Interest
- Agent architecture: Experience with reason–act loops, planning/evaluation/self\-correction patterns, tool/function calling, persistent memory systems, and structured outputs.
- LLM engineering: Familiarity with frontier LLMs and trade\-offs across model families; experience with prompt engineering, context management, and alignment techniques.
- Retrieval and data systems: Understanding of RAG pipelines, embeddings, indexing strategies, chunking methodologies, and grounding techniques.
- Infrastructure and observability: Experience building logging, tracing, monitoring, and evaluation systems for ML/AI applications.
- AI\-assisted development workflows: Leveraging AI tools to enhance engineering productivity and code quality.
- Interest in semiconductor design, EDA workflows, and high\-performance computing environments.
Our Culture
- Challenge the status quo: We are innovators who challenge industry norms and push forward our vision of how silicon should be built.
- Strong opinions, loosely held: We are low on ego, but high on collaboration. We are okay to be wrong and are always open to learning.
- Ship fast, ship quality: We ruthlessly prioritize what matters. We build at lightning speed, but never compromise on the high standards required by the semiconductor industry.
- Proud of our craft: Attention to detail is in our DNA. We take pride in what we build and go the extra mile to ensure an exceptional experience for our users.
*The annual salary range for California is $154,000 to $286,000\. You may also be eligible to receive incentive compensation: bonus, equity, and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the salary range is a guideline and compensation may vary based on factors such as qualifications, skill level, competencies and work location. Our benefits programs include: paid vacation and paid holidays, 401(k) plan with employer match, employee stock purchase plan, a variety of medical, dental and vision plan options, and more.*
We’re doing work that matters. Help us solve what others can’t.
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Salary Context
This $154K-$286K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cadence Design Systems, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $154K to $286K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cadence Design Systems AI Hiring
Cadence Design Systems has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span San Jose, CA, US, Austin, TX, US. Compensation range: $286K - $331K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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