Interested in this AI/ML Engineer role at Synopsys?
Apply Now →Skills & Technologies
About This Role
Date posted 10/13/2026
Category EngineeringHire Type EmployeeJob ID 19256Base Salary Range $201000-$301000Remote Eligible NoDate Posted 10/13/2026
HIRING LOCATIONS: Austin-TX or Morrisville-NC
We Are
Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow.
We are seeking a highly motivated Cybersecurity Architect - AI Security to help define, architect, and secure the next generation of AI-enabled systems, platforms, and services across Synopsys. The Cybersecurity Architect - AI serves as a senior technical leader responsible for developing and implementing security architectures, standards, and governance frameworks for Artificial Intelligence (AI), Generative AI, Agentic AI, Machine Learning (ML), and Large Language Model (LLM) ecosystems.
You Are
You have spent a decade building security into systems that matter, and now you are watching AI reshape everything you thought you understood about attack surface, trust boundaries, and control. You know that securing a model is not the same as securing an application, and securing an autonomous agent is not the same as securing a model. The difference matters, and you have spent the last few years figuring out what that difference actually looks like in production.
You do not wait for frameworks to mature before you act. You read OWASP LLM Top 10, NIST AI RMF, and ISO 42001, then you go build the thing that actually works in your environment. Prompt injection, data poisoning, model theft, these are not buzzwords to you, they are threat vectors you have modeled, tested, and mitigated in real deployments.
You are comfortable walking into a room of GenAI engineers talking about RAG pipelines and vector databases and walking out with a security architecture that does not kill the project but does protect the company. You push back when a design is too open, and you find a way forward when the answer is not obvious. You think in layers, controls, guardrails, and you know that the best security architecture is one that developers can actually use.
At Synopsys, you will shape how a global technology leader secures AI at scale, across products, platforms, and operations that power the semiconductor industry.
What You'll Be Doing
- Design enterprise AI security architecture patterns and reference architectures for GenAI, Agentic AI, ML, and LLM-based systems across cloud, SaaS, on-premises, and hybrid environments
- Conduct AI threat modeling, architecture reviews, and security assessments for AI use cases, evaluating risks related to prompt injection, data poisoning, model theft, model inversion, membership inference, and agent abuse
- Define security standards and implementation patterns for agentic AI platforms, orchestration frameworks, MCP ecosystems, LLM gateways, and autonomous workflows, including policy enforcement, monitoring, logging, and action controls
- Establish controls for secure model training, fine-tuning, deployment, and monitoring, and drive AI red-team and adversarial testing strategies to validate defenses
- Design and implement Agentic Identity and Access Management (IAM) strategy for AI agents, autonomous systems, MCP servers, and AI-enabled applications, including controls for service access, secrets protection, model registries, vector databases, RAG architectures, API security, and data classification
- Led secure AI and model lifecycle initiatives by defining security controls, architectures, and guardrails for AI platforms, agents, and LLM integrations; implementing protections against AI-specific threats; and driving red-team testing and governance to ensure secure, resilient, and trustworthy AI systems.
- Partner with Product Security, Engineering, Cloud, IT, Legal, Privacy, Data Governance, and Enterprise Architecture teams to establish AI security policies, standards, and governance frameworks aligned with NIST AI RMF, ISO 42001, OWASP LLM Top 10, and emerging regulations
- Research emerging AI threats and defensive technologies, evaluate and implement AI Security Posture Management (AI-SPM) tools, and design secure sandbox environments for rapid security evaluation of new AI models, solutions, and agents
The Impact You Will Have
- Shape the enterprise AI security strategy and architecture that enables Synopsys to adopt AI securely, responsibly, and at scale across a global organization
- Reduce risk to intellectual property, customer data, AI models, and critical business processes through practical security guardrails and resilient design patterns that protect what matters most
- Accelerate secure innovation by providing clear, reusable standards for AI, GenAI, Agentic AI, ML, and LLM solutions that engineering and product teams can implement without friction
- Strengthen enterprise readiness for emerging AI threats through threat modeling, adversarial testing, continuous monitoring, and effective detection and response capabilities that adapt as the threat landscape evolves
- Influence executive and cross-functional decisions by translating complex AI security risks into actionable architecture recommendations and mitigation priorities that leadership can act on
- Advance a consistent, security-by-design culture across product, engineering, cloud, IT, legal, privacy, data governance, and enterprise architecture teams, embedding security into AI workflows from the start
- Protect Synopsys customers and partners by securing AI-enabled products and services that power semiconductor design and innovation worldwide
What You'll Need
- Bachelor's degree in Computer Science, Cybersecurity, Engineering, Information Systems, or related field, Master's degree preferred
- 10+ years of experience in Cybersecurity, Security Architecture, Cloud Security, or related disciplines with a track record of building and securing complex systems
- 3+ years of hands-on experience securing AI, Generative AI, LLMs, or ML systems in production environments, not just research or proof-of-concept work
- Deep technical knowledge of AI and ML security, LLM architectures, Agentic AI systems, RAG pipelines, AI model lifecycle security, AI governance frameworks, cloud security architectures, IAM, API security, data security and privacy, secure software development lifecycle, and security monitoring and incident response
- Experience conducting threat modeling, architectural risk assessments, and security design reviews for AI systems, with the ability to identify and mitigate risks like prompt injection, data poisoning, model theft, and agent abuse
- Demonstrated experience working with executive stakeholders and cross-functional teams to drive security strategy, present risk and mitigation recommendations, and influence decision-making at the highest levels
- Strong familiarity with Kubernetes and container security, MLOps and ModelOps platforms, AI orchestration frameworks, vector databases, and the Model Context Protocol (MCP), as well as leading AI ecosystems including Anthropic, OpenAI, NVIDIA AI, Azure OpenAI, AWS Bedrock, and Google Vertex AI.
- CISSP, CCSP, SABSA, TOGAF, or equivalent certifications, GenAI security-related certifications are a strong plus
Who You Are
- You can explain a complex AI security tradeoff to a VP in two sentences without losing the nuance, and then turn around and whiteboard the technical architecture with an AI engineer
- You do not wait for perfect information or fully mature frameworks, you work with what you have, ask the right questions, build what is needed, and iterate as the landscape evolves
- You push back when a design is too open or a risk is being underestimated, and you do it in a way that moves the conversation forward rather than shutting it down
- You are organized enough to manage multiple AI security initiatives at once, from architecture reviews to policy development to vendor evaluations, without losing track of what matters most
- You stay current on emerging AI threats, attack techniques, and defensive technologies, not because it is required but because you are genuinely curious about how these systems break and how to make them more resilient
- You have worked in semiconductor, software, SaaS, or technology industries where intellectual property protection and customer trust are non-negotiable, and you understand what is at stake when AI security fails
The Team You'll Be Part Of
You will join the Cybersecurity Architecture & Engineering team, a highly skilled group of security professionals focused on protecting the company's intellectual property, sensitive information, and critical business systems. The team is responsible for advancing enterprise security strategies, implementing industry-leading security technologies, and enabling secure collaboration across a global workforce.
Collaboration, innovation, and a
Salary Context
This $201K-$301K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 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 $212,160 based on 13,809 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($251K) sits 18% above the category median. Disclosed range: $201K to $301K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Synopsys AI Hiring
Synopsys has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Morrisville, NC, US. Compensation range: $301K - $301K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.