Design Verification Infrastructure Sr. Staff Engineer

About Marvell Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities.  At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead.  Your Team, Your Impact Marvell Central CAD Engineering is building a new AI-integrated verification design environment, and this role sits at its core. A deterministic Python framework owns everything that decides to pass/fail and everything that must be reproducible — build, run, verdict, coverage, and the quality gates — while an AI layer sits on top for the judgment-heavy work: deciding what to run, triaging failures, and proposing fixes that a human approves. The rule is straightforward: AI proposes, the deterministic framework disposes, a human approves. You will design and own the Python components at the heart of that framework and put them in the hands of the DV engineers who depend on them every day. It is hands-on, high-ownership work with a short path from your code to real impact — the engineers you are building for sit right next to you. What You Can Expect Design and own core Python framework components: the declarative build graph and its importer, the run-record store that makes every run reproducible, coverage merge, and verdict logic   Build the simulator backend abstraction — command generation and capability modeling for Cadence   Xcelium   (MSIE incremental elaboration) and Synopsys VCS — so adding a simulator is a new backend and nothing else changes   Assemble self-contained, token-efficient failure bundles (waveforms, logs, run-record fields, testbench configuration, and source pointers) so downstream agents can debug in one place   Wire the integrations: compute-grid job submission, the results dashboard, CI for the gate-blocking   changelist   path, and the MCP endpoints the agents consume   Support the AI layer without owning any ML: author reusable agent skills and prompts, build evaluation harnesses to measure triage and fix quality, and enforce the deterministic guardrails around the agents   Package, deploy, and operate the flow — roll it out to verification teams across sites, then monitor and troubleshoot in production   Write the docs and reusable procedures that let the rest of the org adopt the flow   What We're Looking For BS or MS in Electrical Engineering, Computer Engineering, or Computer Science, new graduate up to ~5 years of relevant experience; strong new graduates with substantial internship or project work are encouraged to apply   Strong, idiomatic Python: clean, testable code and solid command-line tooling   Comfort on Linux and the command line, and with Git   Solid data-structures fundamentals, including graphs/DAGs — the build model is a dependency graph   Working knowledge of CI/CD   Self-directed: can take a well-scoped problem and deliver a component end to end   Clear written communication; the team is collaborative and distributed across time zones   Preferred   Hardware-verification fundamentals and exposure to   SystemVerilog /UVM   Hands-on with an EDA simulator — Cadence   Xcelium   and/or Synopsys VCS   Coverage concepts: collection, merge, and closure   Comfort using AI coding agents, with a habit of critically evaluating their output   Exposure to MCP or other agent/tool integration   Familiarity with compute-grid job scheduling (LSF, SLURM, or SGE)   Expected Base Pay Range (USD) 127,630 - 191,200, $ per annum The successful candidate’s starting base pay will be determined based on job-related skills, experience, qualifications, work location and market conditions. The expected base pay range for this role may be modified based on market conditions. Additional Compensation and Benefit Elements   Marvell is committed to providing exceptional, comprehensive benefits that support our employees at every stage - from internship to retirement and through life’s most important moments. Our offerings are built around four key pillars: financial well-being, family support, mental and physical health, and recognition. Highlights include an employee stock purchase plan with a 2-year look back, family support programs to help balance work and home life, robust mental health resources to prioritize emotional well-being, and a recognition and service awards to celebrate contributions and milestones. We look forward to sharing more with you during the interview process. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Any applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at [email protected] . Interview Integrity  To support fair and authentic hiring practices, candidates are not permitted to use AI tools (such as transcription apps, real-time answer generators like ChatGPT or Copilot, or automated note-taking bots) during interviews. These tools must not be used to record, assist with, or enhance responses in any way. Our interviews are designed to evaluate your individual experience, thought process, and communication skills in real time. Use of AI tools without prior instruction from the interviewer will result in disqualification from the hiring process. This position may require access to technology and/or software subject to U.S. export control laws and regulations, including the Export Administration Regulations (EAR). As such, applicants must be eligible to access export-