NAVI Protocol
ML/AI Founding Engineer
San Francisco
Sponsorship not specifiedDetected 267 days ago
Machine LearningNLPLLMsRAGAvionicsResearchLeadershipCommunication
About the role
- The data is messy, multimodal, and high-stakes.
- Cockpit audio is mono with overlapping speakers.
- Avionics telemetry arrives in dozens of formats.
Responsibilities
- Build and improve the ML systems that power Navi's automated flight debrief - maneuver detection, performance scoring, safety event identification
- Develop and refine audio intelligence pipelines - speaker diarization, speech-to-text, cockpit audio separation, ATC communication extraction
- Design the AI reasoning layer that synthesizes avionics data, audio, and ADS-B into coherent sortie narratives
- Build evaluation frameworks and feedback loops that continuously improve model accuracy against real-world CFI assessments
- Experience building and deploying ML pipelines end to end - data ingestion, model training, evaluation, inference, and monitoring in production
- You've worked with messy, real-world data and know how to build systems that are robust to noise, edge cases, and domain drift
- You ship. You don't wait for a perfect dataset or a clean abstraction. You build, evaluate, iterate, and improve
- Flight training - earn your pilot's license and build with true domain expertise
Requirements
- Experience with LLMs - fine-tuning, prompt engineering, retrieval-augmented generation, or building LLM-powered applications
Nice to have
- Familiarity with aviation systems, flight training operations, or defense technology environments
- Experience with audio diarization, speaker separation, or cockpit/radio audio processing
- Background in safety-critical ML systems where model accuracy has real-world consequences
- Impact you can see - your work will be used by pilots, flight schools, airlines, and the U.S. Air Force
- A role that scales into technical leadership as we grow
- We don't wait for permission or perfect information.
- Ideas come from anywhere regardless of title.
- Figure it out, ship it, iterate.
Benefits
- Early-stage equity - real ownership in a category-defining company
- 5+ years of professional experience in machine learning, AI, or applied research - with production systems, not just papers
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This listing is sourced directly from NAVI Protocol's careers page and normalized into a canonical job model.