[Hiring] Fullstack AI Software Engineer @Futureproofing

Role Description

Own AI products and features from problem definition through production.

• Work directly with founders, executives, and subject-matter experts.
• Design and build systems using LLMs, agents, retrieval, structured data, APIs, and traditional software.
• Make thoughtful tradeoffs across quality, evaluation, latency, cost, reliability, and complexity.
• Build evaluation and feedback loops that tell us whether AI systems are actually working.
• Move quickly from prototype to production without sacrificing engineering quality.
• Push back when a technical or product direction doesn’t make sense—and explain why.
• Use AI deeply in your own engineering workflow while applying strong judgment and guardrails.

Qualifications

• 5+ years of professional software engineering experience.
• Experience owning and shipping real production products.
• Meaningful experience building AI-powered products in production.
• Strong Python and/or TypeScript/JavaScript skills with comfort across the stack.
• Experience with modern AI architectures such as agents, tool use, RAG/retrieval, structured generation, and evaluation.
• Strong product judgment and comfort operating with significant autonomy.
• Clear communication with both technical and nontechnical stakeholders.
• Genuine curiosity about AI—you actively experiment with new tools and continuously evolve how you build.
• We especially value engineers with 0→1 experience, startup backgrounds, customer exposure, or ownership beyond a narrow technical specialty.
• You don’t need to know everything. You do need to be rigorous and deeply curious.

Requirements

• Engagements may be part-time or full-time, with some US working-hour overlap.
• You’ll work closely with Futureproofing’s engineering leadership while working on a dedicated client founding team.

Benefits

• Small, senior teams. We build teams around problems, not seats.
• Real ownership. Engineers participate in architecture, product decisions, tradeoffs, and client conversations—not just implementation.
• Production AI. We care about systems that survive contact with real users, data, and business constraints.
• AI-native engineering. We expect our engineers to experiment constantly with how AI changes the way software is built.
• Intentional matching. We get to know our engineers and clients deeply rather than matching through keywords.
• Developer-first. We’d rather build a smaller network of exceptional engineers doing meaningful work than optimize for placement volume.