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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+.
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.
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.
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.
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.
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.
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.
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.
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.
Zapier · Retool · Stripe · Cloudflare · Twilio · Modal · Langchain · Anthropic · OpenAI · Cursor (cursor.com) · Replicate · Vercel · Datadog · Wiz · Snyk · DataRobot · Antimetal · Postman · Superblocks · EliseAI
Oasis Security · Vanta · Drata · Tines · Apiiro · Adaptive Security · Stytch · Hebbia · Hex · Assembled · Sublime Security · DataDome · Actively AI · DataRobot · Butter · Harbor 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.
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.
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.
Engineering blog posts or "Day in the Life" 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.
MCP is the connective tissue of the agentic web. Runlayer is the security layer for that connective tissue. 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.
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.
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.
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.
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.
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.
Full ownership from problem to delivery. No PM buffer, no committee, no permission needed. You work inside the stack you're securing.
Direct access to the people who built MCP. 5M+ secured calls. Real customers who depend on what you shipped. AI fluency as the default.
Takes raw Brighthire transcripts from recruiter phone screens and converts them into structured hiring manager review documents automatically: takeaway, summary, strengths and weaknesses, and role alignment cross-referenced against the career ladder, JD, salary budget, and market benchmark comp by geography, company size, and industry. Auto-submitted on behalf of the interviewer in Ashby, triggers notifications immediately.
Zero admin, zero delay between screen and HM review. The expectation is a 24-hour SLA turnaround.
Takes a candidate profile that is undiscovered outside of LinkedIn — GitHub, conference talks, open-source contributions — and generates personalized outreach referencing their specific work. Adapts by persona and seniority.
46.6% response rate over 6 months — nearly 2x the LinkedIn benchmark of ~25%. The lift comes from specificity, not volume.
After interview panels close: synthesizes all feedback, flags scoring inconsistencies across the same competency, surfaces unsupported opinions, produces a hire/no-hire recommendation. Separates "signal" from "confidence."
Debrief conversations are faster and more honest — panelists come in having seen where they diverge, not ratifying a consensus formed before the meeting.
Weekly digest: candidates gone cold, at risk of losing to a competitor, ready for an offer push. CRM intelligence applied to the candidate pipeline.
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.
When an offer is extended: surfaces the candidate's stated priorities from recruiter screen notes, generates personalized closing talking points, and flags any comp gaps vs. competing offers. Keeps close rates high without requiring the recruiter to remember every conversation detail from weeks earlier.