Overview
Go-To-Market Engineer
Location:Hybrid (NYC)
Level: IC4-IC5 (Senior to Staff)
Reports to: VP of Marketing / Chief Revenue Officer
Compensation: $140K-$200K base + equity + performance bonus
The Role
We’re looking for a Go-To-Market Engineer who can build AI-powered workflows that turn marketing campaigns from weeks-long projects into hours-long sprints. You’ll design the systems that connect our AI tools, CRM, and data warehouse into a self-improving growth engine.
This isn’t a traditional marketing ops role. You’re building the infrastructure that lets our team operate at Level 3 (autonomous AI agents), not just Level 1 (ChatGPT for brainstorming). You’ll experiment with cutting-edge AI tools, measure what works, and teach the rest of the team how to use them.
Think of this role as: Marketing Engineer + Prompt Designer + Data Scientist + Process Architect
What You’ll Do
Build AI-Powered GTM Systems (Levels 2-3)
- Design and deploy agentic workflows that autonomously research prospects, draft personalized emails, update CRM, and trigger follow-up campaigns
- Connect tools like Clay, Relevance AI, Zapier, and LangSmith into end-to-end automation pipelines
- Build evaluation systems that measure prompt quality, cost-per-task, and conversion rates across different AI models
- Create “self-healing” workflows with multi-LLM fallbacks and error detection
Example Project: Build a system where inbound demo requests automatically trigger: (1) prospect enrichment via Clay, (2) personalized video generation via HeyGen, (3) CRM update, (4) Slack notification to sales, (5) 3-touch nurture sequence—all without human intervention.
Own Data Quality & Revenue Analytics (Level 2)
- Establish the “source of truth” for all GTM data across CRM, marketing automation, product analytics, and data warehouse
- Design deduplication rules, field taxonomies, and data contracts that ensure AI workflows run on clean data
- Build closed-loop attribution tracking from first touch → opportunity → revenue → profit
- Calculate CAC payback, pipeline velocity, and LTV for every campaign and channel
Remember: No AI workflow is trustworthy if the underlying data is dirty. You’re the guardrail.
Design Knowledge Systems (Levels 1-2)
- Build RAG (retrieval-augmented generation) systems that let the team “ask anything about our customers, campaigns, and content”
- Create persistent context stores using Notion AI, Rewind, and vector embeddings of 10,000+ artifacts
- Design progressive summarization pipelines: call transcripts → chunk-level tags → executive dashboards
- Maintain a prompt library with performance metrics for every use case
Example: Sales rep asks, “What objections did we hear from enterprise SaaS companies last quarter?” Your system instantly retrieves relevant call snippets, win/loss interviews, and email threads.
Run Experiments & Build Evals (Level 3)
- Design A/B tests for AI-generated copy, landing pages, and outbound sequences
- Create “golden datasets” with 100+ examples to catch quality drift when prompts change
- Build regression tests that run automatically when new AI models launch
- Track token costs, latency, and error rates across GPT-4o, Claude, and other models
- Implement human-in-the-loop review for high-stakes outputs (contracts, pricing, legal content)
Tools you’ll use: Braintrust, Humanloop, Patronus AI, LangSmith, Giskard
Enable the Team & Manage Governance (Levels 1-3)
- Run weekly “prompt office hours” where you help marketers debug failing automations
- Build training curriculum: onboarding bootcamp → certification path → advanced workshops
- Design ethical guardrails: PII masking, bias detection, content approval workflows
- Document everything in a public changelog so the team knows what’s changing and why
- Create SOC-2-style audit logs for model decisions on sensitive content
Governance frameworks you’ll reference: NIST AI RMF, ISO 42001, SOC-2
What Success Looks Like
30 Days:
- Audit current AI tool usage and identify 3-5 high-ROI automation opportunities
- Map all GTM data flows and document where data quality breaks down
- Ship your first agentic workflow (even if it’s simple)
60 Days:
- Launch 2-3 AI-powered campaigns that beat human-created baselines
- Establish cost tracking and observability for all AI tool usage
- Build the team’s first RAG-powered knowledge base
90 Days:
- Reduce campaign launch time from weeks to hours for 50%+ of campaigns
- Cut AI tool costs by 20%+ through model selection and prompt optimization
- Train 10+ team members on prompt engineering and workflow design
6 Months:
- Campaign velocity: 2x faster launches with equal or better performance
- Cost optimization: 30%+ reduction in AI spend while maintaining quality
- Team adoption: 80%+ of marketing team using AI tools daily
- Revenue impact: Measurable lift in pipeline velocity and CAC payback
You Should Have
Required Experience:
- 3-5 years in marketing operations, revenue operations, growth engineering, or data engineering
- Hands-on AI experience: You’ve built real workflows with LLMs, not just played with ChatGPT
- Technical chops: Comfortable with APIs, webhooks, SQL, Python, and no-code tools like Zapier/Make
- Data literacy: Can build dashboards, write SQL queries, and explain statistical significance
- Experimentation design: You know how to run A/B tests, calculate sample sizes, and avoid p-hacking
Preferred Experience:
- Built workflows using Clay, Relevance AI, Bardeen, or similar AI-native platforms
- Worked with vector databases and semantic search (Pinecone, Weaviate, ChromaDB)
- Experience with observability tools (LangSmith, Helicone, PromptLayer)
- Managed offshore teams or agencies for data/ops work
- Worked at a high-growth startup where you wore multiple hats
The Right Mindset:
✓ Analytical rigor over prompt cleverness — You design experiments, not just write prompts
✓ Governance-aware — You build guardrails before things break
✓ Bias toward action — You ship messy V1s and iterate, rather than planning perfect V10s
✓ Teacher mindset — You enable others, not hoard knowledge
✓ Comfortable with ambiguity — This role didn’t exist 18 months ago; we’re making it up as we go
Tools You’ll Work With
AI & Automation:
- LLMs: GPT-4o, Claude 3.5 Sonnet, Perplexity
- Workflow platforms: Zapier, Make.com, Relevance AI, Bardeen
- Agentic tools: Clay, Lindy AI
- Voice/video AI: ElevenLabs, HeyGen, Descript
Data & Analytics:
- CRM: Salesforce, HubSpot
- Data warehouse: Snowflake, BigQuery
- BI tools: Tableau Pulse, Looker
- Observability: LangSmith, Helicone, PromptLayer
Knowledge & Context:
- RAG platforms: Notion AI, Rewind
- Document processing: Claude 3.5 Sonnet, GPT-4 Vision
- Data enrichment: Clay, ZoomInfo, Apollo
Testing & Governance:
- Eval frameworks: Braintrust, Humanloop, Patronus AI
- Compliance: BigID, OneTrust
- Security: Lakera Guard (prompt injection detection)
How This Role Fits
You’re NOT:
- A traditional Marketing Ops person who just manages Marketo and Salesforce
- A pure data analyst who builds reports but doesn’t ship code
- A prompt engineer who only writes ChatGPT queries
You ARE:
- The bridge between marketing strategy and technical execution
- The person who makes AI useful, not just trendy
- The force multiplier who turns 1 marketer into a team of 10
You work closely with:
- Marketing Ops: You build the automations they need but don’t have time to code
- RevOps: You ensure CRM data quality and attribution tracking
- Product Analytics: You integrate product usage data into marketing workflows
- Sales Enablement: You build tools that help sales close faster
Compensation & Benefits
Base Salary: $140K-$200K (based on experience and location)
Equity: 0.15%-0.5% (early-stage) or competitive RSU package (growth-stage)
Bonus: 20-30% based on campaign velocity, cost savings, and team adoption metrics
Benefits:
- Unlimited AI tool budget (within reason—we track ROI)
- $2,000/year learning stipend for courses, conferences, certifications
- Flexible remote work with quarterly team offsites
- Health, dental, vision, 401(k) match
- 4 weeks PTO + 10 holidays
Our Values
Contrarian Empathy:
We design campaigns from the buyer’s perspective, not ours. We kill our own ideas before the market does.
Analytical Rigor:
We measure everything. We trust data over intuition. We build evals before we ship prompts.
Transparent Governance:
We publish changelogs. We admit mistakes. We don’t hide AI usage from customers.
Human Connection:
AI accelerates our work but doesn’t replace relationships. We still do customer interviews, win/loss calls, and in-person meetings.
Builder Mindset:
We ship V1s fast, learn from failures, and iterate publicly.
Interview Process
Stage 1: Screen (30 min)
Quick intro, walkthrough of your best AI project, discussion of your GTM philosophy
Stage 2: Technical Challenge (Take-home, 2-3 hours)
- Build a simple AI workflow: inbound lead → enrichment → personalized email → CRM update
- Document your approach, tools used, cost analysis, and failure modes
- Submit as a Loom video walkthrough + GitHub repo or no-code workflow link
Stage 3: Live Workflow Design (60 min)
- We give you a real business problem (e.g., “reduce demo no-show rate by 20%”)
- You whiteboard an AI-powered solution in real-time
- We probe on: data requirements, cost/benefit, governance, failure scenarios
Stage 4: Team Fit (45 min)
- Meet with marketing, sales, and RevOps leaders
- Present a 5-min “teaching moment” on an AI concept or tool
- Q&A on collaboration style and working norms
Stage 5: Founder/Executive Chat (30 min)
- High-level discussion on AI strategy and career goals
- Mutual “is this the right fit?” conversation
Timeline: 2-3 weeks from application to offer
Apply
Send the following to Harry.Joiner@EcommerceRecruiter.com:
- Resume (but we care more about #2 and #3)
- Portfolio project: Link to 1-2 AI workflows you’ve built (Loom video + doc/repo)
- “Why this role” note: 200-300 words on why you want this specific job, not just any AI role
Don’t have a portfolio? Build something this weekend:
- Option A: Automate lead enrichment using Clay + GPT-4o
- Option B: Build a prompt library with A/B test results
- Option C: Create a RAG system over your past work emails/docs
We review applications weekly and respond to everyone within 5 business days.
This is a new role category. If you think you’re 70% qualified, apply anyway. We’ll figure out the other 30% together.