Design Verification Infrastructure Sr. Staff Engineer

Marvell Technology•Santa Clara, CA

3d•$127,630 - $191,200

About The Position

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.

Requirements

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

Nice To Haves

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)

Responsibilities

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