Close

• Overhaul how Close syncs email and calendars. Our sync engine has run for a decade; we're making a serious, ground-up investment in its performance and reliability. • Take voice agents further into the call. We own the live conversation layer for Chloe's calls, and there's a lot of work ahead expanding our voice agent capabilities and infrastructure. • Solve inbound. So far Chloe makes calls; next she answers them. An inbound conversation can go anywhere — which raises real questions about specialized agents, a receptionist layer, and seamless call routing between them. • Build native scheduling. Core calendar scheduling capabilities inside Close, plus more powerful scheduling for our agents. • Design human orchestration of an agentic workforce. Agents create work for humans too — things to review, approve, respond to. • Push connect rates and answer rates. Phone number pooling, local presence, branded calling, reputation monitoring. • Move our streaming and event substrate forward. Kafka, Redis, pub/sub.

• Ship code execution for the assistant. The assistant decides when writing code beats answering non-deterministically — today that's generating charts and tables in Python on the fly; next it's reusable user-defined tools and calling external APIs to pull in whatever data the task needs. • Build the eval and observability layer that tells us when an agent is getting better. Unit tests don't cut it for non-deterministic output. We run evals and tracing (LangFuse and our own tooling) as the bar for shipping: if we can't measure it, we don't ship it. • Push generated UI forward. The backend increasingly decides what the user sees — the LLM picks the right presentation (table, chart, widget) and renders it in the assistant, with a full-screen experience and stored, referenceable artifacts on the roadmap. Effectively: customers generate the reports we used to hand-build, one custom report at a time. • Take Custom Agents from prototype to GA. Event-driven agents that act on what's happening inside the CRM in real time — an email lands, an agent drafts the reply from knowledge sources and context, the user approves. This is where we differentiate from the general-purpose assistants: we see the events, we have the context. • Make deterministic and non-deterministic systems work together. Sales processes need steps that happen every single time; LLMs are bad at that. You'll help fuse our Workflows engine with agentic steps so customers get reliability where it matters and intelligence where it helps. • Handle the edges that make agents trustworthy. What happens to a fleet of running agents when a customer's AI credits run out? Pause semantics, recovery, and making sure nothing places a hundred calls that were supposed to happen last week. • Pick the right model for the job. We use many providers, test new models constantly, and are moving toward cost-aware routing — simple summarization jobs shouldn't run on frontier-priced models. You'll call when something is production-ready and when it's still a demo.

• Run our GRC program — then automate it. • Own how customer data moves through our stack. • Be the security gate for new tools and vendors. • Make Close best-in-class at communicating trustworthiness. • Coordinate product security audits. • Investigate and clean up security incidents. • Own our identity and device security. • Run our employee security program.

• Own the four components of monetization: pricing, packaging, billing, and placement. • Move free-to-paid conversion. This is your primary owned metric. • Define and iterate on our AI credit system. • Push our pricing and packaging into its next chapter. • Partner with engineering on the billing system itself. • Run rigorous experiments. • Be the connective tissue between EPD and GTM. • Contribute to the North Star: getting Close from 11k paid customers to 20k by end of 2027.

• Own everything between traffic and trial. The website roadmap, the landing page architecture, the messaging on every conversion surface, the trial signup flow, the pricing page. • Build the testing machine, then run it. Most CRO programs ship 2-4 experiments a month. We expect you to ship more - because you've built agent-assisted workflows for hypothesis generation, variant production, QA, launch, and readout. • Make AI core to how the website actually works. Personalized landing experiences by source. Copy variants generated and tested in hours instead of weeks. • Win at LLM-powered search. Build the systems that get Close cited by ChatGPT, Perplexity, and Gemini for the queries that matter. • Connect cleanly into product activation. The trial signup flow hands off to the Product Growth team. You make sure the website → trial → activation path feels like one elegant experience, not three. • Prove it's working. Visitor → trial conversion, trial signup volume, experiment velocity and win rate.

• Treat demand as the product you are building. Define what you are optimizing, what the leading indicators are, what the experiments queue looks like. Run the function the way a good PM runs a product surface - hypotheses, instrumentation, weekly shipping, postmortems on what did not work. • Pick the channels worth betting on, build them, and iterate. We don't have an astronomical budget, but this is a moment where smarts can out-savvy deep pockets, and Close has the raw stuff to pull it off; we have a product people love, content worth distributing, and an org that's already deeply AI-forward. For the right person, this is all the kindling you'll need. • Own top-of-funnel traffic and care about what happens after. Your primary metric is getting more of the right people to Close's website and product. The "the right people" are those who convert to trials and eventually pay. You'll partner with the Product Growth team to ensure a smooth and successful handoff from website to product. • Run the experiments. Channel selection, ad copy, landing pages, video, social, partnerships, product-led loops. You are setting them up, monitoring the data, and sharing the learnings. • Prove it's working. Traffic up. Trials up. Paid subscribers up. You'll build the reporting to show what's not working and what is.

• Own a portfolio of ~150 partners and their revenue output. You'll develop account plans, run regular business reviews, and be directly accountable for the partner-sourced revenue number. • Recruit and onboard 15-30 new partners per quarter. You'll identify partners who fit Close's ideal partner profile - agencies, SIs, AI and GTM consultants - bring them in, get them trained, and get them to their first deal in under 60 days. • Turn activation into a repeatable motion. Right now, we have 1,500+ partners and a fraction of them are active. You'll figure out what makes a partner go from signed up to actually selling, and build the systems to make that happen at scale. • Enable partners to sell Close effectively. Product training, sales playbooks, demo support, competitive positioning, deal strategy - you'll equip your partners with what they need to win, and you'll be the one coaching them. • Coordinate (and co-sell) with Close's direct sales and CS teams. When a partner brings a deal, you'll work cross-functionally to make sure it closes smoothly. You'll build the internal relationships and processes that make co-selling actually work. • Build the playbook. As the first person in this seat, you're setting the standard for how Close manages partner relationships. The tiering strategies, the engagement cadences, the incentive structures, the reporting - you'll define what good looks like and create the foundation the team scales on.

• Virtually train and onboard new and existing customers • Proactively reach out to high-potential new customers • Research to identify high growth and at risk customers • Communicate effectively via multiple platforms (sms, call, email, Zoom, social) with all Close customers, including co-founders, executives, and sales teams • Work closely with Success and Sales teams on seamless customer transitions • Identify expansion opportunities and upsell customers • Host and manage recurring office hours, group trainings, and interactive demos to support many customers at once • Identify customer content needs and create FAQs, white papers, videos • Communicate with customers in our online Community and via our Helpdesk software

• Conceiving, designing, building, and launching new user-facing features • Implementing new AI features to turbocharge our customers’ daily tasks • Improving the performance and scalability of our GraphQL and REST API • Improving how we sync millions of sales emails and calendar events each month • Working with Twilio's API, WebSockets, and WebRTC to improve our calling features • Building user-facing analytics features that provide actionable insights based on sales activity data • Improving our Elasticsearch-backed powerful search features • Improving our internal messaging infrastructure using streaming technologies like Kafka and Redis • Building new and enhancing existing integrations with other SaaS platforms like Google’s G Suite, Zapier, and web conferencing providers • Enhancing our web app with real-time updates powered by GraphQL Subscriptions • Leveraging the latest AI LLMs from providers like OpenAI, Groq and AssemblyAI

• You will be joining the Infrastructure Team at Close. This team builds and maintains the platform that runs all Close systems (and do we have a lot of those). • Work with us and you’ll be working with: - Multi-terrabyte MongoDB, PostgreSQL, and Elasticsearch clusters - Telemetry systems built on Grafana’s LGTM stack and ClickHouse processing over 130 TB per month - Multiple Kubernetes clusters running tens of thousands of pods - Github Actions & ArgoCD powered CI/CD that can go from merged, to production, to rolled back in 10 minutes - A system that is stable, up to date, and hasn’t needed scheduled downtime in 4 years