GTM Engineer

FlowGen Labs•San Francisco, CA

1d•$150,000 - $250,000•Onsite

About The Position

FlowGen Labs is hiring a hands-on GTM Engineer to build systems that help the company find prospective customers and convert interest into revenue. Working directly with the founders and go-to-market team, this role connects data, software, and AI across prospecting, marketing, and sales. The engineer will have broad ownership, identify operational constraints, build practical solutions, and remain accountable for deployment, adoption, and measurable results. The role includes automating workflows that turn customer signals into qualified pipeline; owning data enrichment and CRM record quality; developing account scoring based on the ideal customer profile; and building AI agents and internal tools for account research, meeting preparation, call summaries, proposals, and RFP or security questionnaire drafts. It also involves integrating CRM, enrichment, outbound, and internal systems through APIs and dependable data pipelines, and operating production code with reviews, logging, monitoring, retries, and error handling. The engineer will work with company founders and the sales team to identify repetitive work, test improvements, and measure effects on pipeline quality, conversion, and time spent selling. The position is in person in the San Francisco Bay Area and is full time. Total compensation is $150,000–$250,000 per year, including base salary and equity; the final package depends on experience and scope. Visa support and sponsorship are available.

Requirements

At least 1 year building software or technical systems in GTM engineering, growth engineering, revenue operations, sales engineering, or a closely related role.
Strong Python and TypeScript skills, including working with APIs and asynchronous workflows, designing database schemas, and debugging production issues from logs.
Hands-on experience with a modern go-to-market stack, including CRMs, enrichment tools, and outbound platforms; understanding of how accounts and leads move through a sales process and how to maintain trustworthy data.
Practical experience building with LLMs, including prompt design, evaluations, structured outputs, and failure handling.
Strong SQL skills and sound judgment about data models, integrations, and pipeline design, including consideration of duplicate records, API limits, concurrent updates, and scaling volume.
Strong business judgment and clear communication; ability to scope ambiguous sales or marketing problems, explain tradeoffs, and connect technical work to business outcomes.
Ownership mentality and follow-through on details, including willingness to handle both new builds and small operational fixes.
Curiosity about enterprise AI, agentic workflows, and how large companies operate.

Nice To Haves

Experience building CRM integrations.
Experience building production AI applications.

Responsibilities

Build automated workflows that convert customer signals into qualified pipeline, including account discovery, enrichment, prioritization, lead routing, and outbound activation.
Own enrichment end to end, turning inbound interest, target account lists, and event contacts into accurate, deduplicated CRM records with useful account context and clear next steps.
Develop account scoring around the ideal customer profile using company data, buying signals, and AI research.
Build AI agents and internal tools for account research, meeting preparation, call summaries, proposals, and RFP or security questionnaire drafts.
Integrate the CRM, enrichment providers, outbound tools, and internal data systems through APIs and reliable data pipelines; own data models, synchronization logic, and data quality.
Ship and maintain production code with clear reviews, logging, monitoring, retries, and error handling.
Debug and resolve broken integrations, failed data jobs, and unreliable model outputs, addressing underlying causes.
Work with founders and the sales team to identify repetitive work, test approaches, and measure effects on pipeline quality, conversion, and time spent selling.
Continuously improve systems as the company scales.