Job Description We're hiring a Lead GTM Engineer to run our multi-channel outbound motion—hands-on in the stack. Summary - Flexible Title: Can tailor to reflect your skills & experience. - Flexible Time: Can do full-time, part-time, side-gig (off-hours), or fractional (contract). - Flexible Commitment: Can do short-term, long-term, or intermittent. Why so flexible? We're a FUNDED startup racing to launch end of Q2 2026. That gives us just 3 months to stack features while raising additional working capital. Feel free to jump in, help us ship, then bounce >> or stick around. A successful launch translates into lots of permanent jobs for those that want them. We're also interested in long-term "side gig" relationships, if that's what you're into—in our experience, a few expert hours often beat full-time learning-curve hours. About Us We're a credible, funded, remote-first startup led by a serial technical founder, backed by a 20-person team. The product is live in private alpha. Learn more at list-lab.org. About The Role We have a large, multi-industry audience, proven channels, and a defined strategy—this phase is execution, automation, and scale. We're hiring someone to run the engine. You own outbound across email, social, and community as one scalable system. AI is core—automation, messaging, optimization. You build, launch, and iterate in the tools; you own execution decisions and how performance improves. Architect-level support is available if you need it—you're trusted to lead, move fast, and not wait on every spec. High execution, high ownership, with room to grow into broader leadership. If you want end-to-end outcomes and to show executional and strategic range—this is it. You'll answer: How do we technically execute this funnel? How do we automate, track, and scale it? How do we connect data, tools, and workflows into one system that actually runs? Possible Tech Stack: Apollo, Clay, Smartlead, Instantly, Lemlist, PhantomBuster, Dripify, Drippi.ai, Warmy.io, GoHighLevel, SendGrid, Zapier, Make, n8n, HubSpot (or Pipedrive), and AI tools for personalization and automation. You won't just use our stack—you'll help define what the "best" options are for our use case. Compensation Up to $275,000 max total annual compensation in Tier 1 cities; cash and equity components to be negotiated (amount reflects combined cash and equity components). What Success Looks Like in 30 Days - Multi-channel pipeline live — email, LinkedIn, plus one other (Facebook, social DMs, or community) - Stack connected: CRM, outreach tools, enrichment, analytics in a unified workflow - Domain health and deliverability under control; sequences running with reporting on opens, replies, meetings booked - Dashboards in place; early experiments producing data What Success Looks Like in 60 Days - Playbooks executed and documented; others can run the systems - Bottlenecks identified and addressed; iterating fast on data - At least one channel producing qualified leads with clear handoff What You'll Do - Build and connect the stack — CRM, outreach, enrichment, automation (see Possible Tech Stack above); move leads from first touch to qualified handoff - Implement tracking — UTMs, attribution, pipelines, dashboards; measure by channel, spot bottlenecks - Own deliverability — domain warmup, email infrastructure, list hygiene, sequence iteration - Run LinkedIn, Facebook, and X automation — configure tools, avoid platform violations, vary copy and cadence with AI - Run experiments and document playbooks — A/B sequences and landing pages; iterate on data; surface what works and what's breaking What We're Looking For Must-haves: - Hands-on execution — you build and run; not strategy-only or deck-only - Technical proficiency with the GTM stack — you've shipped with these or similar tools - Multi-channel outbound experience — email, LinkedIn, or social DMs at scale - Pipeline thinking — you see the whole flow and connect the pieces - Proven AI expertise in outbound — production track record applying AI to personalization, automation, agents, or copy at scale. Baseline for the role: you're expected to operate this way from day one—not learn it on the job. - Fast iteration — hypothesis → experiment → data → decision, in days not weeks Nice-to-have: - Appetite to own more of the motion over time and ship without a spec for every move (not required day one) - Experience collaborating with GTM operators, SDRs, or vendors - Venture-backed startup experience What You're NOT - Not a delegator — day one you're doing the work yourself; you may add support over time, but this isn't a management role. If you'd rather delegate than build, this isn't the right fit.

• Assist in developing and maintaining applications using C#, .NET, Node.js, and Python. • Participate in requirement analysis and contribute to technical discussions. • Collaborate with senior developers to implement features and resolve issues. • Provide application support and troubleshoot problems as needed. • Write clean, maintainable, and efficient code under guidance. • Perform basic testing and debugging of applications. • Document code changes and maintain project documentation.

• Partner closely with Product Managers and users across commercial and corporate functions to transform pain points and proposed solutions into actionable, well-scoped technical requirements for the development team. • Focus on designing and architecting automation and Artificial Intelligence (AI) solutions that streamline workflows, enhance productivity, and deliver measurable business value. • Collaborate with cross-functional teams to ensure technical feasibility, alignment with business goals, and successful delivery of innovative solutions. • Scope and document solution requirements, ensuring alignment with organizational strategy and technical best practices. • Design end-to-end solutions leveraging automation and AI to optimize business processes and drive operational efficiency. • Facilitate technical discovery sessions and workshops with stakeholders to clarify requirements, constraints, and success criteria. • Develop architectural diagrams, technical blueprints, and integration plans to guide implementation. • Guide and support development teams throughout the solution lifecycle, ensuring quality, security, and scalability standards are met. • Validate delivered solutions against business requirements, coordinating with Product Managers and stakeholders for acceptance testing and feedback. • Maintain comprehensive documentation and contribute to a library of best practices for requirements scoping and solution delivery.

• Design, build, and deploy AI/ML solutions using GCP Vertex AI, BigQuery ML, and AI Platform for model training, deployment, and serving • Develop and maintain platform services for AI agent orchestration, RAG pipelines, and LLM integration using Python, FastAPI, and Flask • Collaborate with cross-functional teams to support CI/CD pipelines for ML models, GitLab workflows, and MLOps automation • Work with GCP services including Cloud Functions, Cloud Run, Dataflow, and Pub/Sub to build event-driven AI architectures • Build and optimize data pipelines using BigQuery, Dataflow, and Apache Airflow for AI model training and inference • Contribute to automation, observability, and reliability initiatives for AI systems using GCP Cloud Operations and New Relic • Lead and mentor team members in AI/ML engineering best practices, fostering a culture of learning and innovation • Explore and integrate cutting-edge AI capabilities (LLMs, vector databases, prompt engineering) into platform solutions • Develop customized AI solutions and integrations to meet development teams' requirements, leveraging GCP APIs, SDKs, and LangChain • Create technical documentation, tutorials, and training materials to support AI/ML adoption and facilitate knowledge transfer • Stay up to date on latest AI/ML technologies, GCP products, and industry trends, serving as a subject matter expert for the organization • Analyze existing AI workflows and processes to identify areas for optimization and efficiency gains • Implement scalable AI systems and tools to automate repetitive tasks, streamline ML operations, and enhance developer productivity