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 CadenceXcelium(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-blockingchangelistpath, 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 toSystemVerilog/UVM - Hands-on with an EDA simulator — CadenceXceliumand/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.