Applied AI Platform Engineer (Memory, Data, and Decision Infrastructure)

Compensation: $150k – $175k

Applied AI Platform Engineer (Memory, Data, and Decision Infrastructure) Company: Civilization Labs Location: Remote (US time zones preferred) | NYC optional Reports to: CEO

Civilization Labs Most important decisions are still made with a mix of instinct, politics, incomplete evidence, and rationalization. Civilization Labs exists to change that. We're building decision-grade validation tools and systems that help teams pressure-test important choices with clarity, evidence, and explicit uncertainty, not narrative or false confidence. Our aim is simple to say and hard to build: turn consequential decisions into auditable outputs people can actually use and stand behind.

The Opportunity Civilization Labs is still early enough that the right people will shape not just the product, but the standard behind it. In this role, you’ll help turn our vision into something real: useful software, durable systems, and product experiences that hold up under real-world use. This is a chance to do meaningful zero-to-one work on a system designed for decisions where the cost of being wrong is high.

What will be doing

• Architect and build core platform services on Civilization Labs’ proprietary stack — the data, memory, and retrieval layers that turn messy inputs into auditable, decision-grade outputs.
• Design and own our memory and knowledge systems: how evidence is stored, versioned, retrieved, and carried across the decision lifecycle, with provenance and uncertainty treated as first-class.
• Apply generative AI and agentic systems to real decision-support workflows — integrating, orchestrating, and evaluating models against what actually holds up in use.
• Make end-to-end architecture decisions across data stores, services, and infrastructure, and carry them from design through production.
• Collaborate closely with product owners and the wider engineering organization to turn hard problems into shipped, reliable systems, refining agile stories and tasks along the way.

What you will need

• 5+ years building and operating production software platforms, backend, or distributed systems at scale.
• 3+ years in systems and application architecture, with real ownership of design decisions and their tradeoffs.
• Hands-on experience designing data and storage layers — relational, document, and vector/embedding stores — and the retrieval or memory systems built on top of them.
• Experience integrating and evaluating AI/ML or generative-AI systems in production, or comparable applied research, innovation, and comparative analysis in fast-moving technical domains.
• Comprehensive understanding of the Software Development Lifecycle (SDLC) for commercial products.
• Proven experience with agile development methodologies on significant projects.

Who you are

• Strong organizational skills and keen attention to detail.
• Excellent communication and collaboration skills, thriving in a team-oriented environment.
• Proactive, eager to tackle technical challenges through research and hands-on experimentation.
• Strong problem-solving skills with the ability to pivot and adapt to emerging priorities.
• Fluent with modern coding agents (Claude Code, Cursor, GitHub Copilot) and able to get real leverage from them.
• Expertise coding with Python and TypeScript/Node.js — the core of our stack.
• Familiarity with a compiled language is a strong bonus, as is experience compiling to WebAssembly (WASM).
• Comfortable across data stores — relational (e.g., Postgres), document, and vector/embedding databases — and thinking carefully about data modeling, provenance, and lifecycle.
• Skilled and eager to explore the tools and frameworks behind modern generative-AI and agentic systems.
• Experience with front-end frameworks like Next.js and React — enough to build and own product surfaces end-to-end.
• Familiarity with modern developer tools, including VS Code and Git, for efficient coding and collaboration.
• Solid understanding of microservices frameworks, databases, messaging platforms, and monitoring tools (Prometheus, Grafana).
• Hands-on experience with CI/CD pipelines, cloud providers (GCP in focus), and infrastructure-as-code tools (e.g., Terraform, Helm).

Education

• Bachelor’s degree in Computer Science or a related field.
• Additional software development and engineering certifications are a plus.

Our Culture The biggest calls people make, the strategies, plans, and decisions they can't easily walk back, still often get committed on narrative and gut feeling, and the cost lands on real people. We're here to help pressure-test that thinking before anyone commits. That belief shapes the product, and it shapes our culture: we want to do serious work with people who trust evidence over narrative, chase what's true even when it's inconvenient, and care about the consequences of what they build.

• Integrity: Trust starts with honesty, transparency, and respect for reality.
• Accountability: We do what we say we'll do, and we take responsibility when we miss.
• Collaboration: We solve hard problems together and value clear, generous communication.
• Intellectual Curiosity: We stay interested, skeptical, and eager to improve how we think.
• Social Impact: We want our work to leave people, institutions, and society a little more clear-eyed than we found them, especially on the choices that matter most.

Compensation We’re an early-stage company, and we try to keep compensation simple, fair, and aligned with the reality of the role.

• Base salary: $150,000–$175,000
• We may consider adjustments to cash and equity mix We’ll place candidates within the range based on their track record, technical depth, and how much end-to-end ownership they can take on from day one.

Application process If you’re interested, send your resume, cover letter, and links to your LinkedIn & Github In your email include the following

• Tell us about one product feature or system you personally helped ship. In 5–10 lines, tell us what it did, which parts you owned directly, the stack you used, one important technical tradeoff you made, and what happened once it was in use.
• Tell us about one time you used AI tools, automation, or a new technical approach to solve a real product or engineering problem. In 5–10 lines, tell us what you were trying to do, what you built or tested, how you judged whether it worked, what went wrong, and what you changed as a result.

Email [email protected] Please use subject line: “CL - Applied AI Platform Engineer” - [your name]