R&D Engineering, Sr Engineer

Job Description and Requirements

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.

###

You Are

You have spent the last few years building AI systems that actually ship, not just prototypes that look good in a slide deck. You know the difference between a demo that impresses in a meeting and a production system that runs reliably at 3 AM when no one is watching. You understand that the hard part is not getting an LLM to respond, it is building the orchestration, memory, retrieval, and guardrails that make it work consistently across real workflows.

You are comfortable moving between agent frameworks, backend services, and user-facing components without losing sight of what you are actually solving. When a multi-step workflow fails, you do not just retry it, you trace the state, check the memory, review the tool calls, and figure out what broke three steps earlier. You think in systems, not scripts.

You care about the details that make AI applications reliable: evaluation frameworks that catch hallucinations, observability that surfaces why an agent chose a particular path, memory architectures that actually remember what matters. You do not wait for perfect requirements. You ask the right questions, build something that works, and iterate based on what you learn.

At Synopsys, you will work on agentic AI systems that directly impact how engineering teams design chips and build the technology that powers everything from autonomous vehicles to machine learning hardware.

###

What You'll Be Doing

- Design, build, test, and deploy agentic AI applications using ADK, NAT, LangGraph, LangChain, CrewAI, and OpenCode to create workflow-driven systems that handle planning, reasoning, task decomposition, and multi-step orchestration
- Implement and optimize agent memory systems including semantic, procedural, episodic, working, session, and long-term memory with consolidation and retrieval strategies that make agents actually remember context across conversations
- Build RAG pipelines that combine embeddings, vector search, hybrid retrieval, reranking, and contextual grounding to ground agent responses in real enterprise knowledge bases and documentation
- Integrate agents with enterprise APIs, tools, databases, and search systems using function calling, tool calling, MCP, and connector patterns that make external services accessible to LLM-driven workflows
- Develop backend services, orchestration layers, REST APIs, and user-facing components including human-in-the-loop experiences and operational dashboards that give users visibility and control
- Build automated evaluation frameworks that measure answer quality, memory relevance, tool-use success, trajectory quality, hallucination rates, latency, cost, and safety across production workloads
- Implement observability through tracing, structured logging, prompt versioning, model tracking, workflow telemetry, and incident analysis so you can diagnose failures and improve system behavior over time

###

The Impact You Will Have

- Accelerate how Synopsys IP design teams work by delivering agentic AI systems that automate complex EDA workflows and reduce time spent on repetitive engineering tasks
- Build reliable AI applications that combine LLMs, tools, memory, and retrieval into production systems that engineering teams trust and depend on daily
- Establish reusable patterns, components, and engineering practices that make it faster for other teams to build and ship their own AI-enabled applications
- Improve the quality and safety of AI systems across Synopsys by implementing evaluation frameworks, guardrails, and observability that catch issues before they reach users
- Contribute to the technical maturity of the AI engineering team through code reviews, design discussions, mentorship, and knowledge sharing that raises the bar for everyone
- Help position Synopsys as a leader in responsible, enterprise-grade AI by building systems that balance capability with security, reliability, and ethical deployment

###

What You'll Need

- Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, AI/ML, or equivalent practical experience
- 2+ years of software engineering experience building and shipping production systems, with demonstrated experience delivering AI/ML, Generative AI, or agentic AI applications
- Strong proficiency in Python and working knowledge of at least one of JavaScript, TypeScript, Java, C++, or Go
- Hands-on experience with one or more agentic frameworks such as ADK, NAT, LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent technologies
- Strong understanding of LLM application design, workflow orchestration, prompt engineering, tool and function calling, RAG and Hybrid Graph/RAG architectures, agent memory systems, and single-agent and multi-agent patterns
- Experience with vector databases, embeddings, search systems, reranking, and knowledge-retrieval pipelines that power RAG and semantic search
- Experience building full-stack AI applications including backend APIs or microservices and user-facing AI experiences, plus experience testing and evaluating non-deterministic AI systems, familiarity with AWS, GCP, or Azure, Docker, Kubernetes, Git-based development, CI/CD, automated testing, and Agile practices, and understanding of AI observability, security, privacy, guardrails, and responsible AI principles are all strong pluses

###

Who You Are

- You can take a vague product requirement and turn it into a working agentic workflow without needing someone to spell out every implementation detail
- You move fluidly between designing an agent's reasoning loop, debugging a memory retrieval issue, writing backend API code, and explaining a tradeoff to a product manager
- You write code that other engineers can read, maintain, and extend, and you care enough about engineering quality to push back when a shortcut will create problems three months from now
- You can explain why an agent chose a particular tool or how a memory system decides what to retain in a way that makes sense to both a senior architect and a designer building a user interface
- You are the kind of engineer who digs into production logs at 11 PM not because you have to, but because you want to understand why a workflow failed and how to prevent it next time
- You treat AI systems with the same rigor you would apply to any production software: you think about failure modes, edge cases, observability, security, and the humans who will depend on what you build

###

The Team You'll Be Part Of

You will be part of Synopsys' effort to leverage Generative and Agentic AI to enhance the productivity of IP Design teams. The team is building agents and orchestration platforms that target EDA chip design flows, working at the intersection of cutting-edge AI technology and the complex engineering workflows that power semiconductor development. You will collaborate with other engineers, product managers, designers, researchers, platform teams, and business stakeholders to translate requirements into AI solutions that solve high-value problems for engineering teams across the company.

###

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.

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.