SyndicatedFractional GTM EngineerUnited States

Full-time GTM Engineer, Nash

FULL-TIME GTM EN JOB · MANUFACTURING SECTOR · UNITED STATES

GTM EN · FRACTIONALfractional.quest
Nash
◵ UNITED STATES◷ FULL-TIME◶ MANUFACTURING
Full-time GTM Engineer at Nash, United States
Engagement
Full-timePermanent · as posted
Days / week
Not statedClarify at apply
Day rate
Not statedNot benchmarked
IR35
Not statedClarify at apply

Full-time GTM Engineer at Nash, based in United States. Full posting below, with the day rate stated or estimated with its basis.

01

Full-time GTM Engineer — the posting in full

~/original-posting · verbatimsource: ashby

Full-timeRate not stated Not confirmed still open

What the posting asks · quoted from the listing

  • Must-haveA go-to-market record: outbound, paid, ABM or lifecycle programs you ran, with the pipeline they produced and the systems you built to run them
  • Must-haveSoftware you shipped and kept running: a service, agent or pipeline in production that other people depended on, with something we can look at
  • Must-haveOutbound that scales and still reads like a person: sequences triggered by something that just changed for the buyer and tied to what we sell, reply rates to show for them, and th…
  • Must-havePaid media fluency: you have run budgets and built audiences, and you know which decisions should stay with a person
  • Must-haveHands-on experience applying AI to go-to-market systems: agents, LLM-based automation, MCP-style integrations, and the evals that tell you whether any of it works
  • Must-haveFluency in Python or TypeScript, and daily work with Claude Code, Cursor or equivalent, with specific opinions about where the tooling fails
  • Must-haveIntegration experience: you have built and debugged integrations through APIs and understand auth, rate limits, sync direction and failure modes
  • Must-haveSalesforce as a developer: the data model, the API, and Apex, or the track record to pick it up fast
  • Must-haveData fluency: SQL, warehouse concepts, and the habit of tracing a data quality issue from source to destination
  • Must-haveYou can turn a vague request into scoped work and explain the tradeoffs

THE ROLE

Growth owns the systems that power our go-to-market motion: our CRM, connected to enrichment, sales engagement, outbound agents, lifecycle, routing, visitor intelligence and paid, with GTM OS, our internal platform, tying them together. We are hiring a GTM Engineer to run that motion and build the agents and data layer it depends on.

Nash builds the autonomic logistics platform: a system that senses what is happening across a network, decides what to do about it and adjusts as the results come back, without waiting to be told. Our go-to-market should run the same way. A signal comes in, an agent decides what to do with it, the rep gets the account ready to work, and every outcome writes back so the next decision is better than the last.

Most of the pieces exist. GTM OS is in production, the agents run, the stack is connected. Your job is to turn them into one reliable engine that produces pipeline, hold it to a production bar, then decide what it does next.

WHAT YOU'LL DO

Build and run the AI agents that work across our GTM motion: account research, signal qualification, message construction, routing and reply handling, from design through evals, rollout and the monitoring that keeps them honest

Build the infrastructure to run paid and ABM as one system: audiences generated from the CRM, campaigns launched and paused by rules you write, engagement flowing back into the agents, and spend tied to accounts worked and pipeline created

Own the account context layer every agent and campaign is grounded in: entity resolution, evidence typing, freshness and provenance, and the segment, industry, region and product rules that define our market

Build on GTM OS, our internal platform, and make what it can do available to sales and marketing inside the tools they already use

Be the technical owner of our go-to-market systems, the CRM and everything connected to it, from the data model and routing logic to the integrations and automations that keep the whole stack current and consistent

Own sending infrastructure and deliverability: domain strategy, warming, suppression and sender reputation across every program that sends

Hold the production bar: evals, observability, permissions and auditability, so the system reports its own failures before a rep finds them

Be measured on what the motion produces: reply and meeting rates, pipeline and revenue, cost per account worked. Agent reliability and data quality are how you get there

WHAT YOU'LL BRING

  • A go-to-market record: outbound, paid, ABM or lifecycle programs you ran, with the pipeline they produced and the systems you built to run them
  • Software you shipped and kept running: a service, agent or pipeline in production that other people depended on, with something we can look at

Outbound that scales and still reads like a person: sequences triggered by something that just changed for the buyer and tied to what we sell, reply rates to show for them, and the judgment to pull apart a message that is not landing

Paid media fluency: you have run budgets and built audiences, and you know which decisions should stay with a person

Hands-on experience applying AI to go-to-market systems: agents, LLM-based automation, MCP-style integrations, and the evals that tell you whether any of it works

Fluency in Python or TypeScript, and daily work with Claude Code, Cursor or equivalent, with specific opinions about where the tooling fails

Integration experience: you have built and debugged integrations through APIs and understand auth, rate limits, sync direction and failure modes

Salesforce as a developer: the data model, the API, and Apex, or the track record to pick it up fast

Data fluency: SQL, warehouse concepts, and the habit of tracing a data quality issue from source to destination

You can turn a vague request into scoped work and explain the tradeoffs

HOW TO APPLY

Send us your resume, links to things you have built and kept running (a repo, a live service, an agent, a sequence with its results), and a short note on the system or motion you are most proud of and why. If you would rather skip the cover letter, send a Loom walking us through a piece of work, or a Replit prototype of a GTM agent you would build for Nash. Both are very much encouraged.

Want to grab our attention and bypass the recruiting platform? Record a video introducing yourself at testimonials.nash.ai with code JOIN-NASH. Your application notifies us in Slack the moment it lands, and we get some much needed testing coverage on our latest Claude build :)

More about Nash

  • Nash is the platform that powers modern logistics.

Commerce has inverted. For decades, customers came to where products and services were. Now products and services come to them, on their terms, in real time. That shift has turned every company into a logistics company, even though almost none of them were built to be one. Couriers, fleets, gig workers, parcel carriers, in-store labor, and increasingly autonomous systems all have to be coordinated in real time, against tighter windows and rising expectations, with hard-fought customer trust on the line.

Nash unifies decisioning, execution, and capacity into a single programmable platform. Real-time, AI-native intelligence determines what should happen, operational control executes it, and the platform dynamically orchestrates capacity from any source: a company's own fleets, partners, or the Nash delivery network. Whether a job involves a courier, a gig driver, an internal fleet, a store employee, a technician, or an autonomous vehicle, Nash selects the right resource and manages execution through completion.

We power delivery and logistics for some of the most recognizable brands in commerce, including Walmart, Urban Outfitters, 7-Eleven, and Woolworths, alongside platforms like Shopify and Toast. Over the next decade, logistics will become as foundational to commerce as payments, cloud, and connectivity. Nash is the platform that powers it.

Nash was founded in 2021 by Mahmoud Ghulman (2x Founder, MIT) and Aziz Alghunaim (2x Founder, 2x YC, Ex-Palantir, MIT) and is backed by Y Combinator, a16z, and other top investors. We are headquartered in San Francisco.

What You’ll Love About Us

  • ✅ Early-stage, well-funded startup – directly impact the company and grow your career!
  • ✅ Quarterly broader team on-sites to bond with teammates
  • ✅ Competitive compensation and opportunity for equity
  • ✅ Flexible paid time off
  • ✅ Health, dental, and vision insurance for US employees

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