Senior Software Engineering

Senior Full Stack Software Engineer SalesJack is a fast-growing, profitable sales software platform (CRM, Prospecting Service) purpose-built for independent businesses in the construction supply industry. Our team and customers are spread across the US and Canada. Our mission: build tools that solve real problems for our customers and transform industry workflows. We’re an AI-native engineering team. AI agents are part of how we write, review, test, and ship code every day, and LLMs power real features in our product and data pipelines. As a Senior Full Stack Software Engineer at SalesJack, you’ll be one of the senior technical hires on the team, owning major product surfaces end-to-end. That means more than writing code: you’ll talk to customers, scope what to build, make the product calls, and ship it. We don’t have a PM layer between engineering and customers; You’ll partner directly with the founders and lead engineers to set architectural direction and raise the engineering bar, including how the team uses AI to move faster without lowering quality. This is a hands‑on role: shipping production code daily with AI as a force multiplier, making the calls on what gets built and how it scales, and sweating the details of how it feels to use. Strong opinions held loosely, fast iteration, and a bias toward shipping over deliberating. Own entire product features from “this is a problem worth solving” to “this is live in customers’ hands”: scoping, designing, building, shipping, iterating Talk to customers directly: discovery calls, support tickets, ride-alongs with sales reps in lumberyards. Make the architectural calls on new systems (data model, API surface, frontend composition) and document the reasoning so both the team and our AI tooling can build on it Build across our Node.js stack: Next.js on the frontend, Node services on the backend, BullMQ for background jobs and workflows, PostgreSQL as the system of record Work AI‑first: use coding agents (Claude Code, Cursor, Codex, or similar) to plan, implement, refactor, test, and review, and stay accountable for every line that ships Design and ship LLM‑powered product features: extracting structured data from messy sources, automating rep workflows, surfacing insights from ERP and sales data, with evals, guardrails, and cost/latency budgets that hold up in production Design and operate authentication, authorization, and multi‑tenant RBAC systems that hold up under real customer load, including in agent and LLM‑driven flows Set technical standards (testing, code review, CI/CD, observability, and AI‑assisted development practices) and raise the bar for everyone around you Build reusable AI context for the team: project conventions, agent instructions, skills, and MCP integrations that make every engineer and agent more effective 8–10 years of full stack engineering experience, including time spent owning systems in production at meaningful scale Deep expertise in React and Next.js, with a strong point of view on component architecture, state management, and performance Strong backend chops in Node.js — you've designed and operated services, not just built features inside them Advanced PostgreSQL — schema design, query optimization, migrations under load, multi‑tenant patterns AI‑Native Dev Hands‑on experience building and running AI agents that do verifiably correct work. You design the guardrails that make agent output trustworthy: test suites and type checks the agent must pass, eval harnesses, golden datasets, self‑verification loops, and human‑review checkpoints where they matter. You use AI coding agents as a core part of your daily workflow, not occasionally. keep, and how you verify the output You catch subtle bugs, security issues, and bad architectural suggestions that slip past automated checks, and you know when an agent is confidently wrong You’ve shipped LLM‑powered features to production: prompt design, structured outputs, tool use, and handling failure modes like hallucination, malformed responses, rate limits, and cost overruns You can define what “good” looks like for an AI feature and measure it rather than eyeballing it Familiarity with the current AI tooling landscape (agent frameworks, MCP, model APIs from Anthropic/OpenAI/others) and strong opinions about what’s useful vs. Bonus: you’ve built internal tools, agent workflows, or context systems that made a whole team more productive with AI You can be handed a fuzzy customer problem and come back with a scoped, shippable plan
- not a Jira ticket asking for specs You push back on bad designs, come up with better ones, and have opinions about what “good” looks like in B2B software Customer instinct. You don't need to be shielded from messy customer feedback Fast iteration, direct collaboration with founders, and unfiltered customer feedback Highly competitive salary and comprehensive health benefits #