Founding AI Engineer (YC-backed startup) w/ 0.20% - 1.50% Equity

Job Overview

As a Founding AI Engineer / Member of Technical Staff at a YC-backed public-safety startup, you'll play a pivotal role in designing and deploying machine learning systems that enhance the capabilities of law enforcement's digital analysis. This position involves owning the AI and backend systems that handle vast amounts of evidence, including calls, reports, and documents, transforming them into searchable insights. Your expertise will be crucial in developing retrieval architectures over both unstructured and structured data, making information rapidly accessible for investigators. You will engage in prototyping with advanced models and technologies, refining successful concepts into robust, production-ready systems fronting high-stakes applications. Collaborating closely with Forward-Deployed Engineers and field end-users, you'll translate real-world detective feedback into measurable ML improvements. The role demands engaging across the tech stack, maintaining APIs and evaluation platforms to ensure the AI interface remains reliable and monitored. You have at least three years of professional software engineering experience, particularly with backend systems in a modern stack like Python and TypeScript. Hands-on creation of ML systems, particularly those involving LLMs or deep-learning models, is essential, as is experience with data pipelines and retrieval architectures. You'll be comfortable working end-to-end, from conceptualizing investigator workflows to deploying models in real-world settings, with strong communication skills to navigate a fast-moving startup environment effectively. If you're eager to innovate in public safety tech and comfortable with the dynamic challenges of a startup, this role offers a unique opportunity to make an impact.

Responsibilities

This is a founding AI / backend engineering role. You’ll design and ship the ML systems that power Closure’s “digital analyst” for law enforcement—working closely with the founders, Forward-Deployed Engineers, and investigators in the field.

What you will do:

• Own core AI and backend systems that ingest, process, and search across large volumes of evidence (calls, reports, documents, transcripts, and more).
• Design and implement retrieval / RAG pipelines for unstructured and structured data, making it fast and reliable for investigators to find what they need.
• Prototype with new models and tools (LLMs, embeddings, vector databases, observability stack), then harden the best ideas into production systems agencies can trust.
• Collaborate closely with Forward-Deployed Engineers and users to turn real-world feedback from detectives and prosecutors into concrete ML features and ranking improvements.
• Contribute across the stack when needed (APIs, internal tools, evaluation dashboards) to keep the overall AI surface area robust, monitored, and maintainable.

Qualifications

• 3+ years of professional software engineering experience with strong backend fundamentals (distributed systems, APIs, data modeling) in a modern stack (e.g., Python + TypeScript/React or similar).
• Hands-on experience building and shipping ML/AI systems used by real users, ideally involving LLMs or other deep-learning models (not just research or PoCs).
• Experience with retrieval / RAG or similar architectures over unstructured text or multi-modal data (documents, transcripts, logs), including designing data pipelines and evaluation approaches.
• Comfortable working end-to-end: from understanding investigator workflows and problem framing, to designing experiments, to deploying and monitoring models in production.
• Strong communication and collaboration skills; able to work directly with founders, Forward-Deployed Engineers, and non-technical stakeholders in a small, fast-moving, mission-driven team.

Ideal Candidate

Ideal Candidate Profile

- Field-Driven Engineer
– Strong full-stack engineer (Python + modern frontend) who enjoys leaving the office, sitting with users, and seeing how software actually gets used in the wild.
- Customer-Obsessed Problem Solver
– Comfortable building trust with detectives and agency leadership, asking good questions, and turning messy requirements into clear product and technical decisions.
- High-Ownership Operator
– Thrives in tiny, fast-moving teams, takes full responsibility for deployments and outcomes, and is happy to do whatever the situation requires (from debugging to running training sessions).
- Mission-Motivated
– Energized by improving public safety and the criminal-justice system, and comfortable working with sensitive, sometimes difficult case material.
- Startup-Ready
– Has prior experience in early-stage or talent-dense environments and is excited by ambiguity, rapid iteration, and having a big say in how the product and company evolve.

Company Culture

Closure is a small, seed-stage, YC-backed team built around mission-driven, high-ownership work in public safety. The founders are experienced engineers from Palantir and defense backgrounds who care deeply about helping government work better. The culture emphasizes thoughtful, field-driven product development with investigators and prosecutors, rapid iteration on real-world problems, and a high bar for reliability and trust in how AI is applied. Engineers are expected to be hands-on with users, comfortable with ambiguity, and motivated by impact rather than big-company structure.

Interview Process

1. Initial Call (30 min)
– Introductory conversation with a founder to learn more about your background, walk through the role, and assess mutual fit.
2. Technical Deep Dive (60–75 min)
– Live discussion with an engineer covering your experience building and shipping products, system-design style questions, and how you’ve handled messy real-world requirements.
3. Practical Exercise / Case Study (60–90 min)
– Short take-home or live exercise focused on how you’d approach a forward-deployed problem (e.g., understanding a customer workflow and translating it into product/technical changes).
4. Final Round (60–90 min)
– Conversations with founders and team members to go deeper on collaboration style, working with customers, mission alignment, and any remaining technical topics.