Senior Software Engineer, Applied AI

Key Requirements

• 5+ years of experience building production software systems, with at least 2 years working on AI or ML-driven features in production.
• Deep understanding of modern AI concepts: LLMs, embeddings, retrieval systems, agentic design patterns, evaluation frameworks, and observability for AI systems.
• Strong engineering fundamentals (Python and/or TypeScript/Node.js) and a track record of building reliable, scalable backend services.
• Demonstrated ability to lead technical decisions (architecture, trade-offs, technology selection) and communicate them clearly across engineering and product.
• Experience designing and owning systems under real-world constraints: performance, cost, reliability, and long-term maintainability.
• Ability to take ambiguous, open-ended problems and drive them to well-scoped, shippable solutions with minimal direction.
• High agency: you operate autonomously, make decisions, and flag clearly when you need input.
• Defaults to asking "can an agent or model handle this?" before reaching for a traditional approach, and knows when the answer is no.
• Pragmatic over perfectionist: you know when "good enough in production" beats "perfect in theory," and when it doesn't.
• Low ego, high standards: you hold the bar without holding the room hostage.

Nice-to-Have

• Has shipped real products using AI-assisted coding workflows and can speak concretely to what changed in their engineering process.
• Has designed and operated LLM evaluation harnesses, red-teaming pipelines, or output regression frameworks at scale.
• Active user of (or contributor to) the emerging AI tooling ecosystem: MCP, agent protocols, model routers, LLMOps tooling.
• Experience with the full prompt engineering lifecycle in production: versioning, A/B testing outputs, and monitoring for drift.
• Has rebuilt an internal workflow or tool they previously did manually by replacing it with an agentic system, and can describe what they learned.
• Comfortable reading frontier AI research and forming opinions on what's worth adopting versus what's hype.
• Experience mentoring or technically guiding other engineers on AI system design.