Strong preference for New York City. Hybrid in the Bay Area is possible due to customer proximity
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.
Required Skills and Experience
Qualifications
3+ years of professional software engineering experience with strong backend fundamentals (distributed systems, APIs, and 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.