• Define and drive the multi‑quarter technical strategy for Payments, spanning rails, orchestration, and transfers, to support new products, geos, and significant growth in payment volume. • Architect and evolve the core Payments platform (rails integrations, routing, and funds-flow services) for high availability, low latency, and cost efficiency at global scale. • Lead end‑to‑end design and rollout of large, cross‑team initiatives (e.g., new global rails, decomp/migrations, resiliency programs), breaking high ambiguity into clear milestones and measurable outcomes. • Set and enforce technical standards for financial correctness across Payments, including idempotency, reconciliation, failure‑mode handling, and auditability for all money‑movement paths. • Partner closely with FinHub, Payments Risk, Regulatory Platform, and Product to design cohesive architectures that balance customer experience, risk controls, compliance, and unit economics. • Provide deep technical mentorship to senior/staff engineers across Payments pods, upleveling system design quality, review rigor, and long‑term maintainability. • Use data and observability signals (e.g., success rates, latency, cost, incident patterns) to identify structural platform gaps and lead systematic reliability and efficiency improvements. • Represent Payments in executive and cross‑org forums, clearly communicating tradeoffs, aligning stakeholders on strategy, and securing buy‑in for high‑impact technical bets.

Role Description Built is hiring a Go-To-Market Engineer (GTME) to design, operate, and continuously improve the systems and campaigns that drive lender pipeline and revenue. This role sits at the intersection of campaign execution, marketing automation, AI-driven workflows, data, and enablement. You will not just build GTM infrastructure — you will actively run it day to day. - Own end-to-end campaign execution for the lender business in HubSpot, including email, lifecycle, nurture, paid ads, routing, and lead management programs. - Build, launch, measure, and optimize campaigns based on performance and pipeline impact. - Own day-to-day operation and optimization of the GTM tech stack (HubSpot, Clay, AirOps, Lemlist). - Build and maintain lead scoring models, routing logic, lifecycle stages, and SLAs. - Ensure data quality, attribution accuracy, and system reliability across GTM systems. - Partner closely with Marketing, Sales and Product Marketing to operationalize GTM strategy into execution. - Design, improve, build and maintain automated workflows using tools like n8n, Lemlist, Clay, and AirOps. - Build AI-powered enrichment, outbound, and intent-detection pipelines. - Integrate GTM tools and data sources via APIs and automation platforms. - Replace manual, repetitive processes with scalable, reliable workflows. - Rapidly prototype, test, and iterate on GTM experiments to improve conversion and efficiency. - Own marketing attribution, pipeline analytics, and GTM performance reporting. - Build and maintain dashboards and automated reporting pipelines (Salesforce, HubSpot, Sheets, Looker or similar). - Create playbooks, SOPs, and documentation for GTM systems, campaigns, and workflows. - Train and enable Marketing and Revenue teams on tools, processes, and best practices. - Establish guardrails and operating models to ensure adoption, consistency, and trust in GTM systems. - Act as the internal owner for “how we run Marketing GTM.” Qualifications - 5+ years in Marketing Ops, Revenue Ops, or Go-To-Market / Growth Engineering roles. - Hands-on experience executing and optimizing campaigns end-to-end in HubSpot. - Strong experience with GTM automation and enrichment tools (Clay, AirOps, n8n, or similar). - Salesforce proficiency (reports, dashboards, data structure). - Strong API and integration experience across GTM systems. - Analytical fluency (SQL, advanced spreadsheets, or both). - Strong systems thinking and ownership mindset — you own outcomes, not just tools. - Comfort operating without a fully defined playbook. - Travel up to 20%. Requirements - Experience with ABM platforms (6sense, Demandbase, or similar). - Python or JavaScript for custom scripts and integrations. - Experience using AI/LLM tools for GTM workflows or data processing. - B2B SaaS experience, ideally in fintech or construction tech. Benefits - Salary range: $120,000 - $150,000 USD per year. - Equity in the form of stock options. - Top-notch medical, dental, and vision coverage. - Unlimited PTO policy. - Learning Grant program to support ongoing professional development. - 401k with match and expedited vesting.

• Designing, building, and running cloud-native microservices in Go that are the backbone of our real-time payments platform • Building systems that are highly available, low-latency, and scalable, handling billions of payment requests with precision and resilience • Taking full ownership of services: from initial design and implementation to deployment and production support • Working with a mindset where cost-efficiency, observability, and operational excellence are core to how we build • Collaborating closely with other engineers in a flat, autonomous team structure, with a strong focus on code quality, simplicity, and continuous improvement • Working with modern tooling and technologies such as Golang, Kubernetes, CockroachDB and NATS JetStream in a highly automated DevOps environment

• Design, develop, and deploy AI-driven applications using RAG and Agentic AI frameworks. • Builds/ Optimizes machine learning models • Build and maintain scalable data pipelines and services to support AI workflows. • Implement RESTful APIs using Python frameworks (e.g., FastAPI, Flask) for AI model integration. • Collaborate with product and engineering teams to translate business needs into AI solutions. • Debug and optimize AI systems across the stack to ensure performance and reliability. • Stay current with emerging AI tools, libraries, and research, and integrate them into projects. • Contribute to the development of internal AI standards, reusable components, and best practices. • Work with vector databases, embeddings, and LLMs (e.g., GPT-4, Claude, Mistral) for intelligent retrieval and reasoning. • Participate in code reviews, testing, and validation of AI components using frameworks like pytest or unittest. • Document technical designs, workflows, and research findings for internal knowledge sharing. • Adapt quickly to evolving technologies and business requirements in a fast-paced environment.