Founding Machine Learning Engineer
Role summary
Synapse is seeking a Founding Machine Learning Engineer to build its advanced knowledge graph for clinical research. This role involves developing critical infrastructure and tackling complex problems at the intersection of ML, search, and knowledge graphs. You will be responsible for application features, intelligent ranking algorithms, semantic search and recommendation engines, predictive modeling, real-time data pipelines, RAG systems, and social graph infrastructure. Experience with production ML systems, end-to-end data pipelines, and semantic search is required. Familiarity with AI coding tools and staying current with ML research is also essential. Experience in startups or high-bar engineering environments is preferred.
Synapse is building the world's most advanced knowledge graph for clinical research — helping cardiologists discover and connect around the latest breakthroughs. They're creating personalized feeds that surface what's most relevant, so physicians spend less time searching and more time applying cutting-edge findings.
You're joining a Google and Amazon AI startup with $600K in credits across tools, direct backing from Jeff Bezos, and connections to Amazon's leadership team. Work with an engineering leader who has shipped production models to billions of users — well de-risked with serious capital, strong connections, and enormous room to grow.
- Founding ML Engineer role with massive upside potential. You'll own critical infrastructure from day one and work on hard technical problems at the intersection of ML, search, and knowledge graphs. Strong candidates can move through the full interview process within a week.
About this role
Along with our Founding Full-Stack Engineer, you will build:
- Application features for our web and iOS apps
- Intelligent ranking algorithms that surface the right paper to the right researcher at the right time
- Semantic search & recommendation engines for vector-based systems that understand research meaning, not just keywords
- Predictive modeling systems to forecast which research will gain momentum or shift entire fields before it happens
- Real-time data pipelines that ingest and process millions of papers from PubMed to arXiv
- RAG systems to break down complex papers into structured, searchable components
- Social graph infrastructure that can build feeds, follows, and discussions that scale
Work experience
Has built and shipped
feed ranking or graph-based ML models
to production
Updated
Has owned end-to-end data pipelines and done feature engineering for ML models.
Updated
Experience at a VC-backed startup, FAANG, or other company known for having a high engineering bar.
Updated
Experience at feed-based apps (e.g., Instagram, Reddit, X, Meta, Pinterest, OpenEvidence, ex-Researcher etc.)
Education
CS or related technical degree (Bonus for PhD).
Updated
Hard skills
Uses AI coding tools (Cursor, Copilot, etc.) as part of daily workflow.
Updated
Semantic search experience.
Miscellaneous
At least one clear signal of excellence
(e.g., degree from a top-tier university, hackathon win, experience at a high-pedigree company, or having founded/built something impressive).
Updated
Actively follows ML research and practitioners — knows what's current in the field.
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