GTM Engineer - San Francisco

Giga
San FranciscoPosted 8 March 2026

Job Description

GTM Engineer - San Francisco ABOUT GIGA Giga has recently raised a $61M Series A and has several paying customers, including DoorDash. We’re building the next generation of customer experience - real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world’s largest enterprises. It’s an exciting inflection point for the company. While we have been successful, we have larger ambitions. Our goal is to become the go-to AI platform for all enterprise automation, powered by our voice superintelligence. To achieve this, we need more great engineers. The work affects millions of people every day and our engineers have autonomy and make true impact. This opportunity is unique because we have brilliant founders, have found commercial success, and see a clear path to becoming a generational company. Some further info about us: - Voice AI startup Giga raises $61M Series A https://fortune.com/2025/11/05/voice-ai-giga-raise-61-million-customer-service-series-a/ - DoorDash and Giga Partnership https://www.linkedin.com/feed/update/urn:li:activity:7391576181009018883/ Giga builds AI agents trusted by the largest B2C companies in the world. Industry leaders like DoorDash rely on Giga to automate their most complex support and operations workflows across voice, chat, and email. If being a part of this resonates with you, please apply! ABOUT THE ROLE Giga is seeking a GTM Engineer to architect, automate, and scale the systems that power Giga's revenue engine. This role is for someone who is growth minded and technical. They understand business context and requirements, formulate unique ideas and perspectives, then go execute them from end to end. Working closely with Sales, Sales Development, Solutions Engineering, Marketing, and RevOps leadership, you'll own the technical infrastructure that turns go-to-market strategy into pipeline. You'll integrate modern technology solutions, build with AI, engineer data flows, and deploy intelligent automation across the full GTM motion - from prospect signal detection to customer renewal. The person in this role will make sure our own go-to-market runs with the same precision and intelligence we deliver to our customers. This person must be a self-starter and be able to work with high-autonomy - you'll be building the revenue engine for a company whose customers trust us to talk to millions of their customers every day. WHAT YOU'LL DO - Architect the GTM tech stack: Build an AI native tech stack with seamless integrations. Build a cohesive, automated engine that centralizes data in CRM and eventually data warehouse. (Salesforce, Gong, Clay, Claude Cowork) - Automate what slows us down: Identify repetitive work across the GTM org - account research, CRM hygiene, post-call follow-ups - and replace it with intelligent automation using APIs, webhooks, and AI agents. - Own data integrity end-to-end: Build and maintain the data flows, enrichment logic, and sync architecture that keep our systems accurate, deduped, and trustworthy. - Turn strategy into systems: Partner with leadership to translate GTM strategy into automated playbooks, dynamic scoring models, and signal-based workflows that drive measurable revenue outcomes. - Systematic pipeline generation: Build the outbound engine from scratch - enrichment logic, intent triggers, AI-personalized touchpoints, and human review checkpoints - all wired to a single output: qualified meetings booked. YOU MAY BE A GOOD FIT IF YOU: - Have 3–5+ years of experience operating in a Growth, GTM Engineer, or Revenue Operations role. We are open to candidates that have a technical background that have worked with a GTM team in other capacities. - Are a builder and systems thinker: You want to build moonshot ideas, not just toggle settings. You're the person who sees a manual process and immediately starts thinking about the API call or AI agent that replaces it. You see the GTM stack as an interconnected machine, no ... (truncated, view full listing at source)
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