We have built our venture on our desire to democratize life science knowledge and drive operations forward that can ultimately lead to life-changing innovation.
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· 11 views · 0 applications · 4h
Lead Data Engineer
Hybrid Remote · Worldwide · 5 years of experience · C1 - AdvancedWhy we're hiring a Lead Data Engineer We're building the expert intelligence layer for scientific research: a knowledge graph that connects the world to leading experts based on publications & clinical trials in precise ontologies. You'll design pipelines...Why we're hiring a Lead Data Engineer
We're building the expert intelligence layer for scientific research: a knowledge graph that connects the world to leading experts based on publications & clinical trials in precise ontologies. You'll design pipelines that ingest millions of life-science records, shaping a graph of how scientific knowledge is modelled, enriched, & served.
This is true green-fields work. Your decisions will lay the data foundations for our entire expert intelligence platform.
What You'll Do
You will be working at the intersection of science, data engineering & AI to build expert intelligence.
- Own data end-to-end, design & run data pipelines turning millions of scientific records into a knowledge graph.
- Implement precision entity resolution & enrichment, disambiguate & enrich experts from noisy data sources.
- Utilise LLM workflows where it makes sense, for entity extraction, relationship inference & quality validation
- Develop vector embeddings & semantic search capabilities to power expert discovery & similarity matching.
- Model life-science entities & relationships, ontologies, author networks, publication & clinical trial metadata.
- Build graph & vector data access, performant, accessible, reliable, observable & testable data access.
- Move fast & ship value incrementally, done-and-iterating beats perfect-and-pending.
- Radiate intent & document your thinking openly, collaborating async-first in a hybrid environment
- Lead when you're the expert, follow when someone else is, challenging assumptions when necessary
- Use AI as a daily force multiplier across coding, schema design, debugging, optimisation & validation.
- Destroy your colleagues at Geoguessr (optional but strongly encouraged).
What You'll Need
Technical Skills- Graph Databases: Neo4j, ArangoDB, Neptune; schema design, relationship modelling, query optimisation.
- Python Data Engineering: ETL development; pandas/polars; distributed processing with Spark or Dask.
- Entity Resolution: Deduplication, merging, enrichment across heterogeneous scientific data sources.
- AI-Assisted Data Extraction: LLM entity extraction, schema generation & quality validation.
- Vector Search: Experience with Pinecone, FAISS, Qdrant, or Weaviate; embeddings, hybrid retrieval.
- Workflow Orchestration: Robust, observable pipelines using Airflow or Dagster.
- Data Formats & Standards: Parquet, JSONL, RDF/Turtle; selecting formats for graph & semantic use cases.
- Embedding Models: Understanding of HuggingFace/OpenAI models, dimensionality tradeoffs & cost.
Executive Skills
- Ownership mindset: Treat data & schemas as products powering multiple domains.
- Strategic evaluation: Choose tech aligned with our scale, latency expectations, & roadmap needs.
- Process engineering: Build reliable, repeatable & maintainable workflows.
- Cross-functional communication: Bridge product engineers & scientific domain teams.
- Comfort with scientific data realities: Deep rabbit holes of sprawling complexity.
Strong Bonus
- Life Sciences familiarity: Publication, clinical trial, institutional, ontologies (MeSH, SNOMED, Gene Ontology).
- Hands-on with scientific datasets: OpenAlex, PubMed/MEDLINE, ORCID, Semantic Scholar, ClinicalTrials.gov
Why You Might Hate It Here
- You want predictability & routine.
- You dislike documenting or sharing your thinking openly.
- You see AI as a threat rather than an amplifier.
- You're looking for a "safe" corporate environment - we're not that.
We mean this sincerely: if those points do not work, you'll be happier elsewhere.
Why You'll Love Working Here
- Real Autonomy: You'll own outcomes, not tickets. This is your domain - you'll define data strategy.
- Greenfield Opportunity: Build the from scratch. Your decisions shape our data capabilities for years.
- Mission That Matters: Your work directly enables research - accelerating scientific breakthroughs.
- AI-First Culture: We use AI as a creative & operational partner across every function.
- High Impact: Every domain depends on what you build. Expert coverage directly drives our success.
Success Metrics (6-month target)
- Expert Coverage: Knowledge graph spans 1+ million experts with rich profile data & relationships.
- AI & Platform Enablement: AI & other domains consuming knowledge graph insights.
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· 7 views · 2 applications · 4h
Product Engineer
Hybrid Remote · Worldwide · 5 years of experience · C1 - AdvancedWhy we're hiring a Product Engineer We're building new product domains from zero to market. You'll take ownership of the end-to-end development - from PRD through POC, MVP, to MMP within 6 months. This means shaping requirements, building prototypes for...Why we're hiring a Product Engineer
We're building new product domains from zero to market. You'll take ownership of the end-to-end development - from PRD through POC, MVP, to MMP within 6 months. This means shaping requirements, building prototypes for internal review, and shipping MVPs to real users. You'll operate with high autonomy in our innovation lab while collaborating with core domain & platform teams to ensure your work integrates seamlessly and scales.
This isn't a role for spectators - it's for passionate builders who want to master their craft and push the boundaries of what's possible at the intersection of science, data, and AI.
What You'll Do
You will be working across the full stack to ship user-facing features that solve real problems for real users.
- Take ownership of a product domain from PRD through POC, MVP, to MMP within 6 months.
- Build user-facing features across the full stack - React, TypeScript, Node, GraphQL, Postgres.
- Operate as a self-starter while collaborating with core domain & platform engineers for scalability & integration.
- Use AI tools intelligently to accelerate productivity - we expect 4-10x force multiplication through tools like Claude Code.
- Ship early and often using trunk-based development, feature flags, and continuous delivery practices.
- Challenge assumptions, simplify systems, and continuously ask: "Does this make us faster or smarter?"
- Work with Design Systems using atomic composition & component reuse.
- Embrace fast iteration cycles, balancing experimentation with operational excellence.
- Use data and telemetry to drive improvement, not opinion.
- Lead when you're the expert, follow when someone else is, challenging assumptions when necessary.
Destroy your colleagues at Geoguessr (optional but strongly encouraged).
What You'll Need
Technical Skills- Full-Stack Development: Strong frontend (React, TypeScript, Tailwind); backend proficiency (Node/TypeScript, GraphQL, API design).
- Database & Data Modeling: Postgres; understanding of graph databases (Neo4j) a plus.
- Cloud Infrastructure: Use what makes sense to move fast; platform team supports AWS migration to scale.
- AI-First Development: Comfortable with context engineering & using tools like Claude Code.
- Modern Practices: Trunk-based development, feature flags, continuous delivery.
Product & Execution Skills
- User-Centric Thinking: Translating needs into features that ship.
- Pragmatic Decision-Making: Balancing speed with quality.
- Shipping Experience: 0→1 or early-stage product experience with real users.
- Design Systems: Experience using atomic composition approach & component reuse.
Executive Skills
- High initiative, low ego: Primarily IC/DRI, secondarily collaborator with PM/engineers.
- Systems thinking with bias for action.
- Strong written communication for async, hybrid collaboration.
- Aptitude and learning velocity over years of experience.
Strong Bonus
- Life Sciences familiarity: DeSci, marketplace experience, or scientific research background.
- Domain interest: Expert networks or knowledge platforms.
Why You Might Hate It Here
- You want predictability & routine.
- You dislike documenting or sharing your thinking openly.
- You see AI as a threat rather than an amplifier.
- You need detailed specifications before starting work.
- You're looking for a "safe" corporate environment - we're not that.
We mean this sincerely: if those points do not work, you'll be happier elsewhere.
Why You'll Love Working Here
- Real Autonomy: You'll own outcomes, not tickets. Your domain, your decisions.
- Ship Fast, Learn Faster: Take a product from zero to market in 6 months. That's the mandate.
- Mission That Matters: Your work directly enables research - accelerating scientific breakthroughs.
- AI-First Culture: We use AI as a creative & operational partner across every function.
- Smart, Small Team: No politics, no bloat - just people who care about mastery and momentum.
Success Metrics (6-month target)
- Product Delivery: Ship a new product domain from PRD to MMP with real users.
- User Impact: Validated product-market fit with measurable user engagement & feedback.
Our Tech Stack
- Frontend: React, Next.js, Tailwind
- Backend: TypeScript, Nest.js, GraphQL
- Database: Prisma, Postgres
- Infrastructure: AWS, Docker, GitHub Actions, Turborepo
- Tools: Slack, Linear, Coda + your choice of AI