01
Talent Lead Exercise · Confidential

Runlayer's
Talent
Exercise

Prepared by Priscilla Philavong  ·  May 2026
Please enter your first and last name.
Press Enter ↵ to continue
Non-Disclosure Agreement

NON-DISCLOSURE AND CONFIDENTIALITY AGREEMENT

Unilateral · Candidate Materials
Effective Date: May 26, 2026
Disclosing Party: Priscilla Philavong, New York, NY
Receiving Party: [ You ], authorized representative of Runlayer, Inc., 31 Bond St, Floor 5, New York, NY 10012

The Receiving Party wishes to review confidential candidate materials prepared by Priscilla in connection with the Talent Lead interview process at Runlayer, Inc. (the "Purpose"). In consideration of this disclosure, the Receiving Party agrees as follows:

1 · Confidential Information

All materials, strategies, frameworks, candidate assessments, employer brand frameworks, AI workflow descriptions, sourcing methodologies, and any work product contained in or derived from this Talent Lead Exercise, whether disclosed in written, electronic, or verbal form, constitute Confidential Information.

Excluded: information the Receiving Party lawfully possessed beforehand; information publicly available through no fault of the Receiving Party; disclosures required by law.

2 · Limited Use

The Receiving Party shall use Confidential Information solely for evaluating Priscilla's candidacy and for no other purpose. The Receiving Party shall not reproduce, distribute, publish, or disclose any portion thereof to any third party without Priscilla's prior express written consent. This obligation remains in full force indefinitely.

3 · Ownership

Nothing herein conveys any right, title, interest, or license in the Confidential Information. All strategies, frameworks, methodologies, and intellectual property remain the sole and exclusive property of Priscilla Philavong.

4 · Return of Materials

Upon conclusion of the interview process or upon Priscilla's written request, the Receiving Party shall promptly destroy or return all materials containing Confidential Information.

5 · Remedies

The Receiving Party acknowledges that a breach may cause irreparable harm for which monetary damages are insufficient. Priscilla is entitled to seek injunctive relief and any other available remedies without proving actual damages or posting bond.

6 · Electronic Signature

The Receiving Party's electronic signature shall be deemed valid and legally binding pursuant to the E-SIGN Act and UETA. This Agreement is governed by the laws of the State of New York.

Electronic Signature *
Sign here
🔒 Confidential · Acknowledged by ·
RUNLAYER · Talent Lead Exercise · Confidential

Priscilla Philavong

priscillaphilavong@gmail.com  ·  +1 917.344.9379  ·  May 2026
I didn't try to answer the exercise safely. I ran the actual search, stacked ranked real candidates, built a sourcing thesis, and wrote the employer brand I'd actually use. The AI workflow section is my real stack — not a hypothetical. I spent more than the suggested two to three hours because I wanted to show up, not just submit something. That's how I work.
Section A
Talent Taste — Product Engineer

Candidate Profiles — Senior · Mid · Junior + Extra Credit picks

Levels reflect years of production AI/LLM/MCP experience and map to compensation bands — not total career years. Everyone is titled Member of Technical Staff in NYC. Junior 1–2 yrs · Mid 2–4 yrs · Senior 4+.

Senior · 4+ yrs production AI/LLM/MCP
Eric Anderson — AI Product Engineering Lead, Superblocks (3.25 yrs) → AI Product Engineer, Endgame · Brooklyn, NY
linkedin.com/in/ericanderson777

Why this profile excites me

Superblocks' product does exactly what Runlayer does — governs apps centrally with integrations, authentication, permissions, and audit logs. Eric led the AI product engineering that built it for 3.5 years for enterprise customers. Company size largely translates to wearing multiple hats like working directly with customers.

Confirm early

  • How much of the auth and permissions layer did you own end-to-end vs. contribute to?
  • What did customer interaction look like — direct discovery or specs handed to you?
  • Why Endgame, and what's pulling you to consider something new?
Extra Credit · Senior · 5+ yrs founding team + AI infrastructure
Vikas Unnava — Software Engineer, Hebbia (1 yr 2 mos) → Software Engineer AI, Figma · NYC
linkedin.com/in/vikasunnava

Why this profile excites me

At Hebbia, he built a 25M-document management system from the ground up — FastAPI, Kafka, vector embeddings, Elasticsearch. 500% performance improvement. 200% reliability gain. Before that, five years as founding team at Level: defined the technical vision, led 15+ engineers, personally stabilized a failed rewrite that had already cost $1M in customer churn. Deep AI infrastructure chops and 0-to-1 founding instincts in the same person. That combination is rare. He's 7 months into Figma AI, so approach matters — but the background is exactly right.

Confirm early

  • At Hebbia, were you working directly with customers or purely on the backend infrastructure?
  • What's drawing you to consider new opportunities this early at Figma?
  • Have you shipped anything with MCP or agent frameworks directly?
Mid-level · approaching Senior
Rebecca Krosnick — Software Engineer (Flows), Postman (2 yrs 5 mos) · NYC · She/Her
linkedin.com/in/rebecca-krosnick-315b2180

Why this profile excites me

Rebecca didn't use Flows — she built it. MIT MEng (with security coursework), PhD in HCI from Michigan, six years designing and implementing no-code automation tools using AI heuristics. Then 2.5 years as a product software engineer on the Flows team, writing TypeScript, shipping the visual programming tool that maps most directly to what Runlayer needs to build. She also interned on Apple's AI/ML UI Understanding team. Production engineering chops, product instincts from deep HCI research, and she's shipped tooling developers depend on.

Confirm early

  • What did you own on the Flows team — product and engineering end-to-end, or primarily frontend/TypeScript?
  • Have you worked with MCP or agent orchestration frameworks? Your Flows work is directly adjacent.
  • You just got promoted — what's making you look?
Extra Credit · Mid-level · 2–4 years on AI/LLM workflow tooling
Michael Claus — Software Engineer (Flows Team), Postman (6 yrs 8 mos total) · NYC · He/Him
linkedin.com/in/mclausaudio

Why this profile excites me

Seven years at Postman, 2.5 of them on the Flows team. Built CI/CD and backend infrastructure, shipped LangChain, Pinecone, and OpenAI integrations in production. Non-traditional background — former audio engineer, sales manager, bootcamp grad. That path produces engineers who communicate, who are hungry, and who don't take the job for granted. He's been building AI workflow infrastructure longer than most people in this search. The tenure is a signal, not a red flag — people stay on Flows because the work is hard and the team is good.

Confirm early

  • Walk me through the Flows backend infrastructure you own — what does a day of shipping look like?
  • Your LangChain/Pinecone projects were personal — have you shipped AI integrations in production at Postman?
  • 7 years is a long run — what would make you leave?
Extra Credit · Mid-level · 2–4 years
Rohitha Madduluri — Solutions Engineer III, Cloudflare (2.5 yrs) · NYC · Open to work
linkedin.com/in/rmadduluri

Why this profile excites me

She sells Zero Trust, Workers AI, and LLM security features to enterprise mid-market buyers every day. $3M+ ARR. Deals up to $200K+. She can explain Cloudflare's AI architecture to a CISO and to a developer in the same afternoon. That dual fluency — AI/LLM depth plus enterprise security buyer experience — is the hardest thing to find in this search, and the most directly relevant to what Runlayer's PE does with customers like Gusto and Ramp. UNC CS + Statistics. NYC. Open to work.

Confirm early

  • Show me something you've built — code, GitHub, anything production-facing beyond solution design
  • In your Cloudflare AI/LLM deals, how deep did you go technically — were you writing code or architecting solutions?
  • What's the hardest security objection you've heard from a CISO about AI tooling and how did you handle it?
Extra Credit · Junior / Stretch · Founding FDE, Superblocks · Harvard CS
Julia Curran — Founding AI Forward Deployed Engineer, Superblocks (1 yr 1 mo) · NYC
linkedin.com/in/julia-curran-0a77971a9

Why this profile excites me

Harvard CS. 2.8 years at Thoughtworks — not a typical consulting shop, one of the most rigorous software engineering firms in the industry. Now the Founding AI Forward Deployed Engineer at Superblocks, deployed inside enterprise customer environments to implement Clark, their AI agent for internal apps. The product she deploys every day — centralized governance, integrations, auth, permissions, audit logs — is Runlayer's architecture. She's one year in. The FDE to PE jump will be different, but Harvard CS plus Thoughtworks gives her the engineering floor to make it.

Confirm early

  • As a Founding FDE, how much are you writing code vs. configuring and deploying existing product?
  • What does the hardest enterprise implementation you've led look like — what broke and how did you fix it?
  • You're one year in at Superblocks — what's making you open to something new?
Junior / Stretch · 1–2 years
Melissa Allan — Applied AI Engineer, Workbench AI (9 mos) · NYC · Open to work
linkedin.com/in/mallan9

Why this profile excites me

Her stack is the most current and relevant of any junior profile in this search: LangGraph, multi-agent orchestration, Claude, FastAPI, Pydantic, Pinecone, RAG pipelines. She's not using AI tools — she's building agentic systems from scratch, defining typed contracts and explicit error handling across agent workflows. Prior VMware PM background (1.5 years) gives her product instincts most engineers at this level don't have.

Confirm early

  • Has anyone paid for or operationally depended on what you built?
  • What's the hardest failure you debugged in a multi-agent pipeline?
  • How do you think about building systems an enterprise security team would trust?

Candidate Archetypes — 3 Sourcing Buckets

Bucket 1 — Production AI/integration engineers from developer tools companies
Retool, Cloudflare, Postman, Superblocks, Linear. These engineers build against enterprise APIs at scale, work directly with customers, and translate pain into product decisions. They match the JD's explicit callouts: Salesforce, ServiceNow, Jira, Slack integrations. The strongest profiles in this search came from Superblocks and Postman.

Bucket 2 — AI solutions engineers from LLM companies or working directly on Research teams building in-house rather than relying on 3rd parties
LangChain, Modal, Cursor (cursor.com), Databricks, Unit. MCP ecosystem fluency natively — they've shipped against the stack Runlayer secures. The hardest bucket to find but the highest signal when you do.

Bucket 3 — NYC-based security and compliance engineers
Oasis Security is the sleeper pick here: they secure non-human identity — API keys, OAuth tokens, service accounts — which is almost identical to what Runlayer governs for MCP connections. Also: Vanta, Drata, Tines, Apiiro, Adaptive Security, Stytch, Cloudflare (recent layoffs), DataRobot. Enterprise security buyers, integration-heavy products, NYC culture compatible.

Broad

Zapier · Retool · Stripe · Cloudflare · Twilio · Modal · Langchain · Anthropic · OpenAI · Cursor (cursor.com) · Replicate · Vercel · Datadog · Wiz · Snyk · DataRobot · Antimetal · Postman · Superblocks · EliseAI

NYC-Based

Oasis Security · Vanta · Drata · Tines · Apiiro · Adaptive Security · Stytch · Hebbia · Hex · Assembled · Sublime Security · DataDome · Actively AI · DataRobot · Butter · Harbor IT


Startups with High Technical Talent Density

Cursor (cursor.com) · Early stage

~40 people, hundreds of millions in ARR. Ex-OpenAI team, full ownership, fast shipping. Engineers use and understand MCP clients daily — Cursor is one of 300+ clients Runlayer supports.

Linear · Early/growth

Small team, extreme craft standards. Every engineer ships end-to-end without a PM buffer. Product quality directly reflects team quality.

Wiz · Growth stage

$0 to $100M ARR in 18 months. Unit 8200 alumni — engineers who've operated in high-stakes security environments. Directly relevant talent pool for Runlayer.

Databricks · Growth stage

Strong ML and platform engineering culture. Existing network at Runlayer through GTM leadership — fast path to warm introductions.

Key ingredients at Series A: Technical founders personally in the first 20 hires. Narrow sourcing — 15 right people, not 500 blasts. Technical employer brand in open source and conference talks before the careers page. A fast, respectful process that signals company quality. Equity that rewards early risk.
Section B
Employer Brand — 3 Core Narratives + EVP
1 · "We helped build the protocol. Now we're securing it."

Uniquely Runlayer's — no competitor can claim it. The founding team built the first MCP server at Zapier. David Soria Parra (MCP creator at Anthropic) is on the cap table. Travis McPeak (Head of Security at Cursor) is an advisor. Every employer brand touchpoint reinforces this provenance.

Activation

Founding team publishes first-person pieces about what they saw at Zapier — the blind spots, the attack vectors. Engineers want to work with people who've been inside the problem.

2 · "Runners SHIP — full ownership, no bureaucracy, real stakes."

The people identity (Runners), values (Say it, Ship it, Scale it), and operating principle of intellectual humility point to the same place: we hire exceptional people and get out of their way. 9 products shipped in 8 months. What you build runs at Gusto, Instacart, and Ramp.

Activation

Engineering blog posts from Runners about what they shipped, how fast, and what they learned. The brand is what Runners say about their work — not what Runlayer says about itself.

3 · "The infrastructure moment."

MCP is the connective tissue of the agentic web. Runlayer is the security layer for it. For engineers who think in decades: the infrastructure you build here will be foundational to how AI operates in enterprise for the next 10 years.

Activation

Create presence in the community; hosting Engineering happy hours, going to tech sponsored meet ups, or sponsor meet ups. The engineers in those rooms are exactly who Runlayer needs to hire.



Talent Brand Activations

Interview Process — Map it and publish it.

Candidates use your process as a proxy for culture. A published, transparent process on the careers page signals respect. Top candidates have competing offers and use your process to make decisions. Be the company that tells them exactly what to expect. The AI Coding Challenge shows them we're a progressive tech culture.

Stage 01
Recruiter Phone Screen
30 min
Stage 02
Hiring Manager Screen
45 min
Stage 03
AI Coding Challenge
Take-home
Stage 04
Panel Round
Half day
Stage 05
Offer
48 hr target
Office Tour Video — Show the space, sell the culture.

What to show

  • The actual workspace — not a staged shot. Monitors, whiteboards, how close together people sit.
  • Shared spaces — kitchen, common area, where people decompress.
  • A 30-second clip of what a normal standup or working session looks like.
  • The neighborhood — Bond St is a good address. Show it.

Where it lives

Careers page above the fold, linked from every job posting, and in the recruiter phone screen confirmation email. Candidates who've seen the space before the panel round close faster — they've already pictured themselves there.

The EVP Framework — Runners SHIP

S
Say it
Direct, not diplomatic. Build trust through honesty.
H
Humility
Know what you know. Name what you don't. Close the gap fast.
I
Impact
Shipping is the eval loop. Your work runs at Gusto and Ramp.
P
Protocol
We are the trust layer — for the product and for each other.

Give

Full ownership from problem to delivery. No PM buffer, no committee, no permission needed. You work inside the stack you're securing.

Get

Direct access to the people who built MCP. 5M+ secured calls. Real customers who depend on what you shipped. AI fluency as the default.

"We hire exceptional people and then get out of their way. Runners own their domain from problem to delivery — no handoffs, no bureaucracy, real stakes. If you want to own hard problems at a company defining a category in real time, this is where Runners belong."
Section C
AI in My Workflow

Top 3 ways I currently use AI

1 · Interview Summarizer — used daily

Takes Brighthire transcripts from recruiter phone screens and converts them into structured hiring manager review docs automatically: takeaway, summary, strengths/weaknesses, and role alignment cross-referenced against the career ladder, JD, salary budget, and market benchmark comp by geo, size, and industry. Auto-submitted in Ashby, triggers HM notification immediately. Zero admin, zero delay between screen and HM review.

2 · Outbound Sourcer Agent (Claude)

Takes a candidate profile — GitHub, conference talks, open-source contributions, LinkedIn — and generates personalized outreach referencing their specific work. Adapts by persona and seniority.
Result: 46.6% InMail response rate over 6 months — nearly 2x the LinkedIn benchmark of ~25%.

3 · TA Calibration Agent (ChatGPT)

Synthesizes all panel feedback, flags scoring inconsistencies, surfaces unsupported opinions, produces a hire/no-hire recommendation. Separates "signal" from "confidence."
Result: Debrief conversations are faster and more honest — panelists arrive having seen where they diverge, not ratifying a consensus formed before the meeting.

3 automations I'd build for Runlayer (suggestions from my conversations)

1 · Pipeline Health Agent

Weekly digest: candidates gone cold, at risk of losing to a competitor, ready for an offer push. CRM intelligence applied to the candidate pipeline.

2 · Competitive Offer Intelligence Agent

When a candidate mentions a competing offer: surfaces public comp data, candidate motivations, and draft talking points for the close. Especially important at seed stage competing on mission and equity over cash.

3 · RPS Summarizer rebuilt for Runlayer's stack

The same Brighthire → Ashby loop built at Abnormal, adapted to Runlayer's interview process. Auto-submits structured recruiter screen summaries with role alignment against Runlayer's comp benchmarks and career ladder.

On using Runlayer's product in my recruiting workflow: I use MCP-connected tools daily — Claude Code, Glean, ATS integrations. I'd use Runlayer to set fine-grained permissions so AI recruiting agents can access candidate data but not comp data; audit every AI touchpoint with sensitive PII (GDPR/CCPA); and manage which MCP servers my recruiting stack is authorized to connect to. I can walk through this live using my actual stack.