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Tactiq is hiring a Staff Software Engineer to own meaningful product surfaces end-to-end. You will take incomplete product direction and turn it into production reality: make missing product and technical decisions, build comprehensive versions, ship to production, learn from results, and continue owning as it grows. Initial areas could include MCP and AI-assistant experience, native desktop/mobile apps, AI Dictation, or other important product surfaces.
In this deeply hands-on role, you will own a product area end-to-end including users, architecture, metrics, economics, failure modes, and production behavior. You'll turn intent into product by independently closing the many decisions between product direction and something users can rely on. You'll shape both the what and how by discovering problems and opportunities rather than waiting for enumeration. Work spans frontend, backend, APIs, data, infrastructure, native clients, AI systems, and third-party platforms wherever outcomes lead.
You'll use AI coding agents as execution capacity, delegating implementation, investigation, and testing aggressively while retaining responsibility for judgment and results. You'll increase safe autonomy by improving context, tooling, evals, observability, and guardrails so more useful work happens without constant human supervision. You'll own production behavior by measuring what happens, investigating failures, and iterating based on evidence. Success means finishing things: reducing ambiguity, resisting unnecessary scope expansion, and getting useful results into users' hands.
The tech stack includes TypeScript monorepo, Node.js/GraphQL with Temporal, Firebase, Elasticsearch, React, Chromium extension, native macOS and Windows apps, and increasing AI and agent-driven systems. The company ships in small, reversible changes many times daily using Linear, Mixpanel, Datadog, Claude Code, and Cursor. Tactiq handles sensitive meeting data, so privacy, permissions, access control, and safe AI behavior are first-class product requirements.
Success milestones: productive in product and codebase within one month; materially ambiguous problem from shaping through implementation and observation within three months; clear ownership of important product area within six months; increasingly self-observing area requiring less manual attention over time.
Growth paths include expanding technical and product scope or expanding through people and organizational capability—neither requires giving up being technical.
REQUIRED EXPERIENCE:
- Strong evidence of owning important user-facing products or product areas end-to-end
- History of turning ambiguous or incomplete ideas into production systems
- Strong product judgment and ability to make decisions without continuous specification or approval
- Broad technical judgment across system boundaries with ability to go deep where needed
- Strong modern software engineering fundamentals and production debugging ability
- Substantial hands-on use of AI coding agents as part of normal development
- Familiarity with modern AI application patterns such as tool calling, MCP, agents, evals, context, and permissions
- Evidence of creating leverage through automation, tooling, observability, or systems that eliminated recurring manual work
- Ability to learn unfamiliar domains quickly
