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Open nowPosted 24 hours ago

Growth GTM Engineer, Revenue Operations

Perplexity135 open roles

Pay
$175,000 – $235,000 a year
Where
San Francisco
Work mode
Hybrid
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Your applicationOpen nowGrowth GTM Engineer, Revenue OperationsPerplexity · San Francisco

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The clock on this job

Early applications get read.

14.0% of postings close within 7 days. Measured by our own scanner across the market. Perplexity postings stay open a median of 62 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.7%3 days
  3. 14.0%7 days
  4. 22.5%14 days
  5. 38.3%30 days
  6. 59.0%60 days
This job: posted 24 hours ago

Perplexity median: 62 days open

The posting

In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people and teams adopt Perplexity and Computer on their own, and our marketing programs bring in thousands more leads. To turn that demand into pipeline, every lead has to be enriched, scored, and routed to the right next step within minutes.

We're hiring a Growth GTM Engineer on the Revenue Operations team to own the lead-to-engagement engine. You'll orchestrate enrichment across marketing and product leads.

You'll build the scoring models that combine firmographic and technographic fit with demonstrated intent and product behavior. You'll own the routing that sends each lead back to Marketing for nurture or to BDRs and Sales for engagement. And you'll help build the custom internal apps our reps use to research, prioritize and engage prospects.

You'll report to the Head of Marketing Operations, who sets the priorities for this work and owns our marketing automation platform, campaigns and attribution.

You'll write production code, own the data and logic between our warehouse and Salesforce, and measure everything you build against meetings, pipeline and revenue.

Tech stack: Salesforce | Snowflake | dbt | Python | TypeScript | Clay | Reverse ETL (Hightouch or similar) | Routing and scheduling (Default, Chili Piper or similar) | LLM APIs

WHY PERPLEXITY IS DIFFERENT

- Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious.

- Ownership. You identify the problem, design the solution and ship it.

- Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users.

- Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products.

- Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence.

WHAT YOU'LL DO

- Own enrichment orchestration. Design and run waterfall enrichment across third-party data providers, AI research agents and first-party data for every marketing and product lead. Handle lead-to-account matching, deduplication, field governance, data freshness and provider cost, so every record is complete, accurate and ready to act on.

- Own and iterate on lead and account scoring. Build fit models from firmographic and technographic attributes such as industry, size, region, tech stack, AI maturity and buying-committee coverage. Combine them with intent and behavior signals: web and content engagement, email and campaign engagement from Marketing Operations, third-party intent, event activity, and product usage signals from the Growth team. Calibrate the models against closed-won and closed-lost data, version them, and explain to Sales why a lead scored the way it did.

- Own lead routing. Design the decision logic that sends each lead to Marketing for nurture or to a BDR, AE or Account Manager for engagement, based on score, segment, territory, account ownership, capacity and existing opportunities. Co-define lifecycle stages, handoff SLAs, recycle rules and speed-to-lead triggers with the Head of Marketing Operations, and enforce them in Salesforce and our routing tools.

- Close the feedback loop. Instrument the funnel from lead creation to MQL, meeting, opportunity and closed-won. Find where leads stall, which sources and signals predict pipeline, and whether our qualification criteria still hold, then send those insights back into scoring, routing and Marketing programs. Supply the data behind attribution, which the Head of Marketing Operations owns.

- Help build custom internal apps for prospect engagement. Ship the tools reps use every day, such as account and prospect research briefs, signal-triggered alerts in Slack and Salesforce, prioritized call and outreach queues, and LLM-powered agents that draft personalized outreach and prepare reps before first touch. Run them like products: talk to users, track adoption and retire what doesn't get used. These apps are part of our BDR prospecting toolset, a combination of internal builds and vendor solutions that the Head of Marketing Operations owns or co-owns. Deliver against that roadmap, including integrating vendor tools with Salesforce and our data layer.

- Be the technical owner of the lead data layer. Build and maintain the dbt models, reverse ETL syncs, APIs and webhooks that connect Snowflake, Salesforce, marketing automation, enrichment providers and engagement tools. Make sure data flows are observable, tested and trusted. Support the rollout of our first marketing automation platform by building the Salesforce and warehouse side of its sync.

- Partner across the business. Work with Marketing, Growth, BDR and Sales leadership, Data Science and Sales Systems to turn messy processes into durable systems. Work with the Growth team, using the PLG signals they publish to drive scoring and routing.

WHAT SUCCESS LOOKS LIKE

In your first 30 days:

- Audit the current enrichment coverage, scoring logic, routing rules and lead SLAs, and document the gaps that cost us pipeline.

In your first 90 days:

- Ship an enrichment waterfall with measurable gains in fill rate and accuracy.

- Ship an evolution of our unified scoring model that combines fit, intent and product signals, with a published rubric Sales trusts.

- Ship routing that sends every lead to nurture or sales within an agreed SLA, with dashboards that show speed to lead and conversion by route.

In your first 6 months:

- Ship at least one rep-facing engagement app or agent with sustained weekly use by BDRs and AEs.

- Show a measurable lift in MQL-to-meeting and meeting-to-opportunity conversion.

QUALIFICATIONS

- 4+ years in GTM engineering, RevOps or Marketing Ops engineering, growth engineering, or data engineering in a B2B SaaS environment, including at least 2 years building enrichment, scoring or routing systems.

- Commercial instinct. You start from a revenue problem, like "inbound enterprise leads wait too long for a first touch," and work back to the data, logic or tool that fixes it.

- Strong SQL and experience building data models in a warehouse (Snowflake, dbt or similar). You pull your own data.

- Production code in Python or TypeScript. You've shipped internal tools, integrations or agents that real users depend on.

- Deep working knowledge of the Salesforce data model (leads, contacts, accounts, opportunities, campaigns, ownership), and experience automating within it and integrating with it.

- Hands-on experience with enrichment and intent providers and orchestration tools, such as Clay, ZoomInfo, Apollo, Clearbit, 6sense, Demandbase or Common Room.

- Fluency with funnel mechanics: lead lifecycle design, scoring models, routing and assignment logic, SLAs, attribution data and conversion metrics.

- Comfort with REST APIs, webhooks, OAuth, rate limits and error handling across third-party GTM tools.

- Operator and business partner mindset. You work directly with reps and marketers, ship fixes quickly, and don't need a PM to translate requests.

NICE TO HAVE

- Experience in a hybrid PLG and sales-led motion: product-qualified leads, mapping users to accounts, and sales-assist routing.

- Experience with routing and scheduling tools (Default, Chili Piper, LeanData) and reverse ETL (Hightouch, Census, Polytomic).

- Experience building LLM-powered research, personalization or qualification agents, including evaluation and monitoring.

- Experience with statistical or ML-based scoring (logistic regression, gradient boosting) and model calibration.

- Entity resolution and domain-matching experience at scale.

- Prior GTM Engineer experience at a GTM-tooling or AI-native company.

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