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### 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.
### You Are:
You are an accomplished technical leader, recognized for your deep expertise and visionary thinking in AI and infrastructure\-native architectures. With a rare blend of hands\-on engineering experience and strategic business acumen, you thrive in complex environments where you translate ambitious business goals into robust, scalable, and secure AI solutions. You understand the intricacies of GenAI, agentic AI, and distributed systems, and you are adept at influencing enterprise\-wide technology decisions and investments.
Your passion for advancing AI frameworks and platforms is matched by your commitment to architectural integrity and technical excellence. You are a trusted advisor to both executives and engineering teams, capable of communicating complex ideas clearly and persuasively to diverse audiences. Mentoring senior architects and engineers comes naturally to you, as you elevate those around you and set a high bar for quality.
You thrive in collaborative, cross\-functional settings, unifying technical voices and aligning business needs with sustainable architectural choices. You are comfortable representing Synopsys as a senior technologist with customers, partners, and industry forums. Your long\-term vision, systems thinking, and ability to drive measurable business outcomes make you an invaluable contributor to our enterprise strategy and technical leadership.
### What You’ll Be Doing:
- Defining and maintaining enterprise architecture blueprints for GenAI, Agentic AI, and Infrastructure solutions.
- Architecting large\-scale GenAI and agentic AI platforms to ensure scalability, reliability, security, and extensibility.
- Evaluating and guiding the adoption of emerging AI, infrastructure, and platform technologies.
- Driving architectural alignment across business units to minimize fragmentation and technical debt.
- Collaborating closely with product and business teams to translate requirements into technical architectures.
- Providing architectural oversight for critical programs, high\-risk initiatives, and flagship customer deployments.
- Establishing and reviewing security, compliance, and governance architectures for AI and infrastructure platforms.
- Partnering with external vendors and hyperscalers to shape joint technical roadmaps and integrations.
- Authoring and reviewing architecture decision records and technical strategy documents that guide long\-term execution.
- Mentoring and elevating senior and principal architects, fostering a culture of technical excellence.
### The Impact You Will Have:
- Enable scalable and repeatable AI platform adoption across the enterprise.
- Reduce technical risk and architectural fragmentation through clear standards and governance.
- Accelerate time\-to\-market via reusable, well\-architected infrastructure and AI foundations.
- Improve platform reliability, security, and performance for mission\-critical AI workloads.
- Enhance developer productivity and innovation velocity through strong architectural enablement.
- Strengthen customer confidence by ensuring robust, enterprise\-grade AI solutions.
- Influence strategic technology investments with long\-term architectural foresight.
- Elevate overall engineering quality by mentoring senior technical talent.
- Support revenue growth by enabling AI\-driven products and differentiated capabilities.
- Position Synopsys as a technical leader in agentic AI and infrastructure architectures.
### What You’ll Need:
- Recognized expertise in AI and agentic platforms, infrastructure\-native architecture, and distributed systems.
- Deep knowledge of GenAI, LLMs, agentic AI patterns, and AI system design.
- Proven ability to design and influence enterprise\-scale architectures across multiple teams or business units.
- Strong understanding of security, compliance, and governance in infrastructure and AI systems.
- Exceptional communication skills, with the ability to explain complex technical concepts to executive audiences.
- Demonstrated thought leadership through architecture reviews, technical publications, or patents.
- Experience working with hyperscalers, AI vendors, and strategic partners.
- Strong systems thinking and long\-term architectural mindset.
- Track record of mentoring senior engineers and architects.
- Typically, 15\+ years of experience in software engineering, architecture, or platform development.
- 8\+ years designing and delivering large\-scale infrastructure\-native systems.
- Significant hands\-on experience with AI/ML platforms and GenAI systems.
- Prior experience as a Principal Architect, Senior Architect, or equivalent technical leadership role.
- Demonstrated ownership of enterprise\-critical platforms or architectures.
- Experience supporting both internal platforms and external/customer\-facing systems.
- History of influencing cross\-functional and multi\-organization technical initiatives.
- Experience operating in complex, highly regulated, or high\-availability environments.
- Exposure to multi\-infrastructure or hybrid architectures.
- Consistent record of delivering long\-term technical impact beyond individual projects.
### Who You Are:
- Visionary leader with strong business orientation and technical depth.
- Collaborative, able to unify technical voices and align stakeholders.
- Mentor and coach, elevating the technical bar for senior talent.
- Clear, persuasive communicator across all organizational levels.
- Strategic thinker with a passion for architectural integrity.
- Adaptable and resilient in fast\-paced, evolving environments.
- Trusted advisor to executives, engineering, and strategic customers.
- Committed to diversity, inclusion, and fostering innovation.
### The Team You’ll Be A Part Of:
You’ll join a high\-impact team of senior architects and platform engineers dedicated to advancing Synopsys’ AI infrastructure and ecosystem platforms. This team operates at the forefront of technology, championing architectural excellence and driving enterprise\-wide innovation. The team’s core focus is on building scalable, secure, and extensible GenAI and agentic AI systems, enabling transformative solutions for internal and external customers. Collaboration, mentorship, and technical leadership are central to the team’s culture, ensuring that Synopsys remains a leader in the AI and infrastructure space.
### Rewards and Benefits:
We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non\-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.
\#LI\-TS5
Salary Context
This $233K-$349K 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 ($291K) sits 35% above the category median. Disclosed range: $233K to $349K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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.
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