GTM Automation Engineer

Crexi•Los Angeles, CA

1d•$110,000 - $150,000•Hybrid

About The Position

Crexi is seeking a GTM Automation Engineer to build and operate AI-driven workflows that help go-to-market teams engage brokers, investors, and owners with less manual work. The role converts marketplace activity, CRM records, enrichment, and conversation data into signals and automations used by sales and marketing teams. The engineer takes defined projects from prototype through production and owns their business outcomes, with success measured by meetings, pipeline, revenue, and rep time saved. The role works closely with SDRs, Account Executives, Marketing, Revenue Operations, and Engineering. It includes building prospecting and research automation; maintaining data sourcing and enrichment pipelines; combining activity and business data into scoring and alerts; integrating AI agents and other tools across the GTM stack; measuring impact; and maintaining the reliability, quality, governance, and documentation of production systems. The position is hybrid in Los Angeles, with the listed compensation range applying to candidates in Playa Vista, California.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
At least 3 years of experience in GTM engineering, growth or revenue operations, software or data engineering, or a similar technical role supporting or closely partnering with a revenue team.
Track record of cross-functional work with teams such as Sales, Marketing, Revenue Operations, and Engineering, delivering measurable business results such as pipeline, conversion, or productivity gains.
Hands-on experience shipping LLM-based applications or automations to production users; internal tools count.
Knowledge of Salesforce or HubSpot and tools such as Outreach and Clay, including their data structures and APIs.
Ability to build in Python or TypeScript and develop integrations using version control, testing, and careful deployment practices.
Experience building with large language models, including thoughtful work with prompts, context, tools, and evaluation, and judgment about when AI is not appropriate.
Comfort with SQL and messy CRM and third-party data, including deduplication and account matching.
Understanding of GTM workflows, including prospecting, lead routing, pipeline management, and SDR work.
Ability to take projects from idea to completion within priorities set by RevOps and GTM leaders, and clearly communicate technical tradeoffs to non-technical partners.

Nice To Haves

Hands-on experience with Clay, Search API, Gong, or Qualified, and sourcing and structuring external data at scale.
Experience with Snowflake or dbt, or syncing warehouse data back into a CRM.
Background in experimentation or analytics.
Prior work supporting SDR or outbound sales teams.
Experience in commercial real estate, marketplaces, or B2B SaaS.
A repository, portfolio, or short demo showcasing shipped LLM-based applications or automations.

Responsibilities

Build and own automated workflows for account research, prospect prioritization, and outreach drafting, with human review before customer-facing activity.
Develop and maintain data sourcing and enrichment pipelines that deliver clean, deduplicated records to Salesforce, HubSpot, and downstream sales tools while respecting API limits.
Combine marketplace activity, CRM history, enrichment, and conversation data into signals, scores, and Slack alerts that guide rep engagement and manager coaching.
Integrate LLMs and agent workflows across the GTM stack, including Salesforce, HubSpot, Outreach, Gong, Qualified, Slack, and Snowflake, using simpler rules where appropriate.
Instrument builds and report impact on meetings booked, pipeline created, conversion rates, and rep time saved, in partnership with Revenue Operations on baselines and attribution.
Spend regular time with SDRs, Account Executives, and Marketing, including shadowing prospecting blocks and reviewing Gong calls, to identify problems, define requirements, and improve tools based on feedback.
Own production automation health through monitoring, alerting, retries, and evaluation sets for LLM outputs; diagnose and fix failures, data issues, and model regressions, and escalate broader-impact issues to Revenue Operations.
Follow data governance, security, and privacy standards, including applicable outreach consent and opt-out requirements and responsible handling of customer data shared with AI vendors.
Evaluate GTM and AI tools using working prototypes, recommend whether to build, buy, or extend, and help maximize value from existing tools' native AI features.
Document systems, data flows, prompts, and runbooks so Revenue Operations and GTM leaders can understand, maintain, and extend them.