application_answers / art_cXuw0LG8IEY

Content

{
  "answers": [
    {
      "question": "Do you have 8+ years of experience in Product Management?\n",
      "answer": "Yes. I have 12+ years of product management experience spanning developer-facing platforms, infrastructure, and AI products. At Intuit (2021\u20132024) I was Staff PM for Developer Frameworks & Platform Infrastructure, owning SDK tooling, a self-service developer portal, and platform infrastructure serving 675M+ engagements across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma. Before that, I was Senior PM at Splunk (2019\u20132021) owning Search Orchestration microservices and SPL/SPL2, and SOA Technical PM at Kaiser Permanente (2012\u20132018) leading enterprise platform products including Logging-as-a-Service at 1.7 TB daily volume. Since 2024 I have been building and shipping AI-native products as a founder."
    },
    {
      "question": "While this role touches on AI and LLMs, a huge priority for us is robust developer experience and a strong product mindset. Can you walk me through a time you built a customer-facing product, and how you approach learning new technologies like AI when a project requires it?",
      "answer": "At Intuit I owned the ICE Self-Service platform end-to-end \u2014 DevPortal, GitOps configuration, and ICE Playground \u2014 which reduced developer onboarding from 2\u20133 weeks to minutes in pre-production and under 24 hours for production, while mitigating over $1M in projected opex growth. The product served internal developers across ~20 mobile apps and 30+ SKUs, so I had to balance generic scalable infrastructure with purpose-built guidance for specific teams. I also extended Java and Python SDK Starter Kits with scaffolding templates, build configurations, and CI/CD integration so developers could go from zero to a production-ready microservice in minutes. When AI became central to my work \u2014 first at Intuit and then as a founder \u2014 I approached it the same way I approach any new technology: I build something real with it immediately. For my aeval platform I implemented bootstrap confidence intervals, Welch's t-test, and Cohen's d effect size to bring statistical rigor to LLM evaluation, and for my RL Workbench I implemented 12 RL algorithms (PPO, GRPO, DPO, and others) across TRL, VeRL, OpenRLHF, and NeMo RL with live metric streaming. I find that shipping a working system \u2014 even a narrow one \u2014 compresses the learning curve faster than any other method and surfaces the real product tradeoffs quickly."
    },
    {
      "question": "Are you willing to work 3-days / week onsite in San Francisco location?",
      "answer": "Yes \u2014 I am based in Oakland, CA and am fully available to work onsite in San Francisco three days per week."
    },
    {
      "question": "Are you legally authorized to work in the United States?",
      "answer": "Yes \u2014 I am legally authorized to work in the United States."
    },
    {
      "question": "Will you now or in the future require sponsorship for employment visa status (e.g., H-1B visa status)?",
      "answer": "No \u2014 I do not require and will not require sponsorship for employment visa status."
    },
    {
      "question": "How did you hear about this job?",
      "answer": "LinkedIn"
    },
    {
      "question": "Website/Porfolio",
      "answer": "felixamoruwa.info"
    },
    {
      "question": "Point of Data Transfer",
      "answer": "Acknowledge/Confirm"
    }
  ],
  "skipped": [
    "First Name",
    "Last Name",
    "Email",
    "Phone",
    "Resume/CV",
    "Cover Letter",
    "LinkedIn Profile",
    "At Adyen, we strive to create an accessible and inclusive experience for all candidates.\n\nPlease let our team know if you need any adjustments to the recruitment process to make it more accessible and/or inclusive to your needs."
  ],
  "verification": {
    "pass": null,
    "error": "verification unavailable: BudgetExceeded: Per-call cap exceeded: $0.6210 > $0.50. Raise budgets.per_call_usd in config.toml if intended."
  }
}