Founding AI Engineer ​/ Member of Technical Staff YC - Startup

Founding AI Engineer / Member of Technical Staff YC - Startup

New York City or San Francisco Bay Area

$ - $ (US Dollar)

PS.

- Please apply only if the location is suitable for you and you are willing to travel! Thank you!

As a Founding AI Engineer, you will play a pivotal role in designing and deploying machine learning systems that enhance our digital analyst platform for law enforcement. This hands-on position involves owning the core artificial intelligence and backend systems that process, search, and manage vast quantities of digital evidence, including calls, reports, and documents. Your expertise will be instrumental in designing retrieval and RAG pipelines for structured and unstructured data, ensuring investigators have quick and reliable access to critical information.

You will prototype new models and tools, refining the most effective ideas into hardened systems that can be trusted by agencies. Collaboration with Forward-Deployed Engineers and field users will be vital, as you transform real-world feedback into concrete improvements in machine learning features and performance. You will also contribute across the stack, managing APIs and internal tools to ensure robust and maintainable AI functionalities.

The ideal candidate will bring over three years of software engineering experience, with strong backend and ML/AI development skills, particularly in environments requiring distributed systems and data modeling. Your role will demand comfort with ambiguity, a thirst for impact, and seamless communication with both technical and non-technical stakeholders.

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.

• 3+ years of professional software engineering experience with strong backend fundamentals (distributed systems, APIs, data modeling) in a modern stack (e.g., Python + Type Script/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 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…