Software Engineer

📋 Description

• • Build end-to-end AI features that directly drive revenue, from defining success metrics to shipping and iterating on solutions across React/TypeScript and Node.js/Python stack.
• • Develop and maintain core AI product lines including AI Dialer, AI Prospecting, AI Sequencing, and Agent Studio, used daily by over 1,100 customers.
• • Make foundational technical decisions on agent architecture, such as designing agent memory systems per rep and defining primitives for long-running outbound strategies.
• • Collaborate directly with customers through Slack channels and customer calls to validate features, gather feedback, and rapidly implement fixes.
• • Own the full lifecycle of product features, ensuring they are shipped, monitored, and improved until they meaningfully impact user outcomes.
• • Mentor other engineers through code reviews, design discussions, and pairing on complex technical challenges.
• • Operate in a high-velocity environment where rapid iteration and customer feedback loops are central to product development.
• • Contribute to a product suite that transforms traditional sales software by replacing manual workflows with AI agents capable of autonomous action.
• • Work closely with a team of engineers who are former founders or founding engineers from companies like Applied Intuition, Scale, Airtable, Citadel, and Jane Street.
• • Engage in a culture that values learning from experienced builders, with 13 Olympiad medals represented across the engineering team.
• • Participate in an in-office culture requiring presence at least three days per week in San Francisco.
• • Focus on creating the interface and interaction model for human-agent collaboration in sales workflows, where AI handles repetitive tasks and humans focus on closing.
• • Scale systems to support a business that has tripled ARR annually for three consecutive years and serves over 1,500 customers including Notion, HubSpot, Cursor, and Rippling.
• • Work with production AI systems that require debugging misbehaving agents and implementing evals to prevent recurrence of failures.
• • Translate customer pain points into technical requirements and prioritize features based on real-world usage patterns.
• • Balance strong technical opinions with openness to feedback and iterative improvement.
• • Rapidly learn and adapt to evolving product needs in a fast-growing AI-driven sales platform.