Founding AI Engineer / Member of Technical Staff (YC-backed public-safety startup) w/ 0.20% - 1.50% Equity
Bounty Amount: $12,500 - 20,000
Company Name: Closure
Role Type: Full-Time
Location: Strong preference for New York City. Hybrid in the Bay Area is possible due to customer proximity
Salary / Hourly Rate: $125,000 - $200,000 per year
Benefits: U.S.-based role with visa sponsorship available for strong candidates. (NO H1Bs),Equity: 0.20% - 1.50%,Bonuses based on company performance,We contribute 3% to your 401(k), regardless of your own contribution,Unlimited PTO,Paid Short- & Long-term Disability Insurance,Paid Life Insurance,Criminal Justice Ready: Given the sensitivity of our work, we support employees through FBI background checks / CJIS clearance process as needed.
Role Information
Role Overview: N/A
Responsibilities: 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.
Minimum Requirements: 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, ideally with LLMs or other deep-learning models used by real users (not just research or prototypes).,Experience with retrieval / RAG or similar pipelines over unstructured text or multi-modal data (documents, audio transcripts, etc.), including designing data flows and evaluation approaches. (Based directly on the JD’s “Data Versatility / RAG or similar pipelines” section.),Comfort working in a fast-moving startup environment with high autonomy, ambiguity, and end-to-end ownership of projects.,Strong communication skills – able to partner with founders, FDEs and investigators, explain technical trade-offs, and turn messy requirements into robust systems.
Screening Questions: (Optional Video). This step is completely optional. If you’d like, record a short 2–3 minute video introducing yourself and your experience — or share a recording of your interview with the recruiter if that’s easier. You can upload the link via Loom or Google Drive. This just helps us get to know you better, but there’s no pressure if you’d prefer to skip it.,(Optional Portfolio / GitHub) If available, please share a link to your GitHub, portfolio, or any recent projects you’ve worked on. This is entirely optional but helps provide more context about your work.,Are you comfortable building with AI hands-on and working directly with customers to gather feedback, shape features, and help launch new products? Please share an example of when you’ve done this or how you would approach it.
Company Information
About Company: N/A
Culture: N/A
Additional Information
Interview Process: Initial Call (30 min) – Introductory conversation with a founder to learn more about your background, walk through the role, and assess mutual fit., 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., 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)., 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.
Day to day: You’ll spend your days designing, building, and operating the AI and backend systems that power Closure’s “digital analyst” for law enforcement.In practice, that looks like:• Owning core data and ML pipelines that ingest, process, and search across large volumes of evidence (calls, reports, transcripts, documents, etc.).• Designing and implementing retrieval / RAG architectures over unstructured and structured data so investigators can reliably find what matters.• Prototyping with new models and tools (LLMs, embeddings, vector databases, observability) and turning the best ideas into robust, production-ready systems.• Partnering closely with Forward-Deployed Engineers and founders to turn field feedback from detectives and prosecutors into concrete ML features and ranking improvements.• Contributing across the stack when needed — from APIs and job orchestration to evaluation dashboards and monitoring — to keep the entire AI surface area healthy.
Team: You’ll join a tiny, senior founding team of ex-Palantir and defense engineers who are obsessed with solving meaningful problems in public safety.• You’ll report directly to Gilad Levy (Co-founder & CTO) and work closely with Aaron Zelinger (Co-founder) on architecture, roadmap, and product direction.• You’ll collaborate tightly with Forward-Deployed Engineers who spend time in the field with investigators and bring back high-signal problems and feedback.• The team is lean, highly technical, and mission-driven — everyone ships code, talks to users, and has a strong voice in what gets built next.
Growth: This is a founding-level AI role with a lot of surface area:• Own major pieces of Closure’s AI and backend architecture from day one and shape how the digital analyst evolves over time.• Define best practices for evaluating, monitoring, and hardening LLM / retrieval systems in high-stakes law-enforcement settings.• Influence product strategy by connecting what’s technically possible with what investigators actually need in the field.• Help hire and mentor future ML/AI engineers as the company grows, building out a small but world-class applied AI team.• Build a rare track record of shipping real-world, high-impact AI systems at a YC-backed, public-safety-focused startup.
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.
