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United States Off\-site
Category Product Management Remote Eligible Yes Hire Type Employee Job ID 15524 Base Salary Range $208000\-$312000 Date posted 06/09/2026
### Technical Product Management Architect – HAV AI Platform Strategy
### We Are:
At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self\-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high\-performance silicon chips and software content. Join us to transform the future through continuous technological innovation.
Our Hardware\-Assisted Verification (HAV) platforms—ZeBu and HAPS—enable system\-level verification and validation so customers can validate functionality, performance, power, and software with high confidence. As systems grow in complexity, verification workflows must scale beyond traditional approaches. In this role, you will help make AI a core architectural capability of HAV platforms—improving ease of deployment, verification efficiency, accelerating debug, and enabling new levels of system insight.
### You Are:
You are an accomplished technical product leader with a passion for transforming verification through AI\-centric architecture. Your expertise in hardware\-assisted verification (HAV), especially emulation and prototyping, empowers you to identify where AI can drive step\-change improvements. You think holistically about systems, data pipelines, and productization, ensuring AI is woven into the fabric of platforms—not simply added as an afterthought. You have a proven track record of translating strategic vision into actionable, scalable solutions that deliver measurable customer value.
You thrive in highly collaborative, cross\-functional environments, partnering with R\&D, product management, and go\-to\-market teams. Your communication skills enable you to align executives and engineering teams around complex roadmaps and tradeoffs, and your customer\-centric mindset ensures that every AI investment is grounded in real\-world productivity outcomes. You are comfortable operating at multiple levels—whether drafting detailed PRDs, architecting platform services, or presenting the platform vision to customers and partners. As a trusted leader, you influence through clarity, pragmatism, and a relentless focus on delivering what matters most for customers and for Synopsys’ leadership in the market.
Above all, you are driven by impact—eager to shape the future of verification by embedding AI as a first\-class capability, and excited to work at the intersection of hardware, software, and advanced data\-driven technologies.
### What You’ll Be Doing:
- Identifying, prioritizing, and owning AI\-driven verification use cases that deliver breakthrough improvements in productivity, such as debug acceleration, coverage closure efficiency, and regression throughput.
- Defining the HAV AI platform strategy, focusing on ZeBu and HAPS, and building a multi\-year roadmap for AI\-native HAV capabilities with clear milestones and adoption targets.
- Specifying how AI/ML and agent\-based systems will enhance test generation, debug, coverage, power, and performance workflows across the verification continuum.
- Driving platform\-level PRDs that span hardware, software, data collection/telemetry, model lifecycle, and AI services to ensure seamless integration and operability.
- Partnering with R\&D to architect data pipelines and execution models (host, embedded processor, FPGA\-adjacent, transactor\-based) to optimize performance, security, and scalability.
- Aligning the HAV AI strategy with adjacent solutions (debug, coverage, power, SLM, and post\-silicon) to deliver end\-to\-end verification continuity and value.
- Collaborating with Synopsys.ai and Copilot teams to integrate agent experiences and define shared platform services for HAV workflows.
- Engaging lead customers for early validation of use cases, quantifying value, and driving adoption through feedback\-driven iteration.
- Communicating the HAV AI platform vision in executive, partner, and customer forums to drive alignment and excitement across the ecosystem.
### The Impact You Will Have:
- Set the long\-term direction for AI as a foundational capability within HAV, influencing the future of verification at Synopsys and across the industry.
- Advance ZeBu and HAPS platforms toward AI\-native verification experiences that scale with growing system complexity and customer demands.
- Deliver measurable improvements in productivity, throughput, and time\-to\-signoff for leading system design teams worldwide.
- Create durable product differentiation through unique data, proprietary models, and seamless workflow integration.
- Enable new system\-level, power\-aware, and post\-silicon\-adjacent workflows by connecting insights across the entire verification continuum.
- Drive adoption of AI\-powered HAV solutions through customer engagement, early access programs, and impactful value demonstrations.
- Shape the verification industry by pioneering AI\-driven orchestration, debug acceleration, and coverage intelligence capabilities.
- Establish Synopsys as the leader in AI for hardware verification, influencing industry standards and best practices.
### What You’ll Need:
- 15\+ years of experience in technical product management, engineering, or architecture within EDA/verification (emulation, prototyping, compilers, or related domains).
- Deep understanding of hardware\-assisted verification workflows and platforms, including emulation and prototyping.
- Demonstrated ability to deliver applied AI/ML capabilities in production software or platforms, from concept through launch.
- Working knowledge of ML fundamentals, agent\-based systems, and model lifecycle considerations (data, evaluation, deployment).
- Hands\-on experience with verification workflows, including test generation, debug, coverage, and performance/power analysis.
- Proven track record of building data/telemetry platforms, experimentation frameworks, or model evaluation pipelines (preferred).
- Excellent cross\-functional communication and leadership skills, with the ability to align executives and engineering teams around roadmap tradeoffs.
- BS/MS in Electrical Engineering, Computer Engineering, Computer Science, or equivalent practical experience.
- Customer\-facing experience driving discovery, pilots, and adoption for platform products (preferred).
### Who You Are:
- Visionary platform thinker who connects hardware, software, data, and AI into coherent, actionable product strategies.
- Pragmatic and outcome\-driven, prioritizing initiatives that will ship and deliver measurable customer impact.
- Comfortable operating at multiple altitudes—from hands\-on product documentation to long\-term platform vision.
- Trusted partner to engineering and executive stakeholders, aligning teams through influence, clarity, and decisive leadership.
- Product\-minded leader who frames AI investments around concrete verification use cases and customer productivity outcomes.
- Highly collaborative, open to diverse perspectives, and dedicated to fostering an inclusive, innovative team environment.
### The Team You’ll Be A Part Of:
You will join a highly skilled, cross\-functional product management and architecture team focused on transforming the future of hardware verification. This team partners closely with R\&D, Synopsys.ai, Copilot, and go\-to\-market teams to define and deliver AI\-native capabilities across ZeBu and HAPS platforms. Our culture values innovation, customer focus, and collaborative problem\-solving, empowering every team member to make a meaningful impact on both product strategy and customer outcomes.
At Synopsys, we want talented people of every background to feel valued and supported to do their best work. Synopsys considers all applicants for employment without regard to race, color, religion, national origin, gender, sexual orientation, age, military veteran status, or disability.
In addition to the base salary, this role may be eligible for an annual bonus, equity, and other discretionary bonuses. Synopsys offers comprehensive health, wellness, and financial benefits as part of a competitive total rewards package. The actual compensation offered will be based on a number of job\-related factors, including location, skills, experience, and education. Your recruiter can share more specific details on the total rewards package upon request. The base salary range for this role is across the U.S.
Salary Context
This $208K-$312K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Synopsys, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($260K) sits 21% above the category median. Disclosed range: $208K to $312K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Synopsys AI Hiring
Synopsys has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Sunnyvale, CA, US, US. Compensation range: $300K - $349K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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