salesape-ai - Senior Founding Engineer – AI Learning Platform
Requirements
• We value mindset over specific job titles. You are an exceptional systems thinker who thinks in feedback loops rather than simple product features. You naturally ask yourself: "How does this system get smarter every day?" Above all, you have built learning or recommendation systems that measurably improved from real-world feedback—that experience is the anchor for this role. • Distributed systems, event-driven architecture, and large-scale event processing. • Data engineering, stream processing, feature stores, and robust data modeling. • Graph databases (knowledge graphs) and vector databases for retrieval and memory systems. • Python, TypeScript, SQL, and modern cloud infrastructure (AWS/GCP/Azure). • Recommendation engines, personalization platforms, or reinforcement learning pipelines. • Designing LLM application architectures and robust AI evaluation frameworks (prior GenAI experience is highly beneficial but not strictly mandatory). • We are a lean, ambitious team that values builders who think deeply but move fast. Our core engineering values are: • Curiosity over Certainty: We ask "how does the system get smarter?" rather than assuming we have all the answers. • First-Principles Thinking: We break complex systems down to their fundamental truths to build elegant, novel solutions. • Shipping over Perfection: We believe working software in production teaches us infinitely more than beautiful designs on a whiteboard. • Long-term Compounding over Short-term Optimization: We design systems that build value over years, not just weeks. • Strong Opinions, Loosely Held: We debate fiercely based on data, but commit fully once a direction is set. • Intellectual Honesty & Ownership: We own our mistakes, speak truth to data, and take absolute responsibility for our outcomes.
Responsibilities
• 80% Engineering & Building: You will spend the vast majority of your time architecting, writing, and shipping production-ready code. You will inherit a seeded prototype of our knowledge layer and harden it into a robust, scalable, and resilient production platform. • 20% Technical Leadership & Shielding: You will partner closely with the Senior Leadership Team to ruthlessly prioritize the technical roadmap. You will guide other engineers on architectural standards and act as a protective buffer—keeping them safe from the daily "noise" of a fast-growing startup so they can focus on deep, uninterrupted builder mode. • WHAT YOU'LL BUILD • You will design, own, and scale the architecture behind a continuously learning platform. Specific areas of focus include: • Event collection architecture & customer interaction pipelines to capture rich interaction logs cleanly. • Outcome measurement frameworks to tie AI suggestions to actual business outcomes (sales, retention, clicks). • Recommendation & feedback loops that let the AI automatically improve its behavioral models based on real evidence. • Knowledge graphs, vector databases, and memory/retrieval systems that serve as our persistent cross-product intelligence. • Experimentation infrastructure & feature stores to run secure experiments and manage features efficiently. • Model independence & deployment strategy to architect the system so we can run our own or open-source models on our own infrastructure where it makes sense—a deliberate lever to stay independent of any single LLM provider on both capability and cost. • Evaluation frameworks to continuously benchmark and validate prompt and model improvements. • THE HARD PROBLEMS WE'RE BETTING ON • We will be honest with you: this is a high-risk, high-reward bet, and part of what makes it worth doing is that the core problems are genuinely unsolved. The central question you will help us answer is deceptively simple—can we reliably tell whether something our AI did led to a better business result? Getting there means confronting a few hard problems head-on, and we would rather debate them openly with you than pretend they do not exist: • Causation, not just correlation: knowing what actually worked, and separating the AI's contribution from everything else happening in a business. • Capturing the outcome: much of the success that matters—a meeting booked, a deal won, a customer retained—happens outside our systems and often isn't tracked today. Instrumenting reliable outcome signals is a first-class part of this role. • Transfer across businesses: what works for a life-insurance broker probably isn't what works for a roofer. We need to learn which know-how generalises and which is context-specific, rather than assuming one business can simply teach another. • Enough signal to learn from: building the data foundations and instrumentation so the loop has enough high-quality evidence to improve—expect meaningful groundwork here before the compounding effects kick in. • None of these are reasons not to build it. They are the reasons this role exists, and why we are hiring someone with real learning and recommendation-systems experience to own the architecture that answers them • WHAT SUCCESS LOOKS LIKE • Structured Learning: Every customer interaction automatically translates into structured, usable learning data. • Measurable Performance: Every single AI recommendation can be tracked and measured against real-world business outcomes. • Compounding Defensibility: Every experiment run by one customer improves future recommendations for all other customers safely and securely. • Opinionated AI: Our AI agents become increasingly opinionated, moving beyond basic prompt rules to act on real-world evidence of what works. • Autonomous Improvement: The platform improves continuously over time without requiring manual developer intervention.
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