What Are We Looking For?We're looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a Sr. GTM Engineer, Outbound, you will be tasked with building the AI agents, automations, and integrations that speed up prospecting, sharpen targeting, and surface the signals reps need to close, embedded with the SDR and AE organizations. Success is measured in more qualified pipeline per rep, faster deal cycles, and reps who no longer do the manual version of this work. This is a senior individual contributor role where your leverage comes from what you ship, not from managing headcount, and you will set the craft bar for outbound automation and coach SDRs and AEs as you go.
What Will You Do?Primary responsibilities include:
- Automate prospecting by building agents that research accounts, build lists, and draft personalized outreach at scale for SDRs.
- Build sequencing intelligence, designing workflows that optimize send times, cadence steps, and messaging based on engagement data.
- Surface deal signals by building agents that mine call recordings and CRM activity for risk flags, buying signals, and next-best-action recommendations for AEs.
- Own the outbound stack, integrating sales engagement, conversation intelligence, enrichment, and CRM so rep-facing data stays accurate and actionable, for example Outreach/Salesloft, Gong/Chorus, Salesforce, and ZoomInfo/Clearbit.
- Support forecasting and pipeline hygiene by building automations that flag stale deals, missing data, and forecast risk before they become surprises.
- Make quality measurable by building evals for outreach personalization quality, call-summary accuracy, and signal precision against human baselines.
- Build the guardrails, implementing human-in-the-loop review for anything rep- or customer-facing, plus logging and rollback paths.
- Coach and document, bringing SDRs, AEs, and revenue operations up the AI curve and building playbooks and reusable templates.
- Ramp through a staged first year: in months 1 to 3, embed with one or two priority teams, map the current outbound motion end-to-end across prospecting, sequencing, call review, and deal progression, and ship your first prospecting or sequencing automation with an eval harness behind it; in months 4 to 6, deploy a deal-signal or call-intelligence agent live for AEs and stand up shared templates for outreach, personalization, and account research.
- In months 7 to 12, own a meaningful slice of the outbound automation layer, show measurable gains in pipeline per rep, cycle time, and forecast accuracy, and hand off matured systems to SDR and AE leadership so they run day-to-day on the systems you built and instrumented.
What Skills and Knowledge Will You Bring?Ideal candidates will have:
- 6 or more years across sales development, sales operations, revenue operations, or systems/solutions engineering, with hands-on time building automation or AI into outbound or sales workflows.
- Demonstrated experience designing, building, and deploying AI agents (prospecting agents, call-summary agents, deal-signal agents) that changed real pipeline or cycle-time outcomes.
- Fluency with the outbound stack and how rep-facing data moves through it, including sales engagement platforms such as Outreach and Salesloft; conversation intelligence such as Gong and Chorus; CRM in Salesforce; forecasting in Clari; enrichment and prospecting through ZoomInfo, Clearbit, and LinkedIn Sales Navigator; and meeting scheduling through Chili Piper.
- Strong prompt architecture skills and practical experience with agent frameworks, orchestration platforms such as n8n and Make, and API/MCP integrations.
- Working proficiency in at least one of SQL, Python, or JavaScript, enough to build lightweight tools, integrations, and workflows.
- A builder's instinct paired with GTM judgment: you understand quota, pipeline coverage, and deal stages, and you prioritize by revenue impact.
- Experience building evaluation frameworks for AI systems (prompt evals, benchmarking, regression testing), and practical experience fine-tuning or heavily customizing models for production use cases, is preferred.
- Familiarity with forecasting methodologies and pipeline hygiene practices is preferred.
- Comfort coaching non-technical teammates through AI adoption and anticipating resistance without relying on positional authority is preferred.
- A B2B or enterprise SaaS background, including familiarity with brand-safety and compliance constraints on automated outputs, is preferred.
