Senior Fullstack Engineer

New York, NY · Hybrid · Full-time

$180K–$220K base + competitive top-decile equity

This is a startup discovery engine used by some of the largest and most prolific venture capital firms to source, research, and invest in companies.

The product turns hundreds of fragmented, unstructured sources — APIs, raw HTML, legal filings, and similar data — into structured knowledge about startups, investors, and people.

The company was founded in 2020, has raised $75M, and is built by a 45-person team of ex-founders and engineers from Google, LinkedIn, Microsoft, and Meta.

Discovery is the intelligence layer behind the platform. It starts with large-scale ingestion and ends with the search and research experience investors use to find breakout companies before they are obvious.

On top of that foundation, the team is building an agentic research system that lets users explore the startup ecosystem through natural language and structured queries.

Flow is a deal management system built directly on the knowledge graph. It is designed to be the first place investors track companies, deals, and collaboration instead of syncing data between disconnected tools.

That makes the data model and workflow model part of the product, not an internal implementation detail.

This is a full-stack role for someone who wants ownership across product, data, and infrastructure. Depending on fit, you will work on Discovery or Flow, but in either case you will be responsible for the surfaces customers use and the systems that make them work.

You will work directly with engineering leadership and domain experts. The expectation is not that you wait for perfect specs; it is that you turn ambiguous product goals and messy data into production software with clear tradeoffs.

If you have operated at senior or staff scope in product engineering, data-intensive systems, or AI-assisted workflows, this should feel familiar.

These workflows are only as good as the data beneath them.

The system has to reconcile noisy external sources, maintain canonical entities, serve low-latency search, support interactive research, and keep product state consistent as users act on top of changing data.

That creates tradeoffs around normalization, indexing, latency, correctness, and developer velocity that the best candidates will recognize immediately.

• Source ingestion and normalization: turn APIs, HTML, filings, and other raw inputs into structured entities the product can query and trust.
• Knowledge-graph-backed product surfaces: build experiences in Discovery or Flow that are tightly coupled to the underlying model.
• Search and research workflows: improve retrieval, filtering, ranking, and end-user ergonomics for complex exploratory use cases.
• Deal and collaboration logic: own the APIs and state transitions that make Flow a system of record rather than a sync target.
• Data and API design: define models that support both internal correctness and user-facing speed without fragmenting the platform.
• Production quality: improve observability, debugging, and reliability so issues are visible before they become customer problems.
• Applied AI integration: help shape natural-language and structured-query experiences on top of the graph.

You are likely a strong fit if you:

• Have shipped full-stack systems and been accountable for the outcome, not just a component.
• Are comfortable moving between Python, Go, TypeScript, React, SQL, and infrastructure without losing rigor.
• Can make architecture decisions in the presence of messy data and incomplete requirements.
• Care about latency, correctness, and maintainability in the same conversation.
• Can explain technical tradeoffs to non-engineers and still hold the line on engineering quality.
• Want direct exposure to a customer problem where product, data, and AI meet.

• Backend: Python, Go
• Frontend: TypeScript, React
• Data: Postgres, dbt, Elasticsearch
• Infrastructure: Google Cloud Platform, Terraform
• AI: Groq

The stack is mixed because the product spans ingestion, search, and end-user workflows. The right person should be comfortable moving across those layers.

The company is at the stage where one senior engineer can still shape core architecture, but the product is already important enough that data quality and performance are not hypothetical concerns.

That is the opportunity: build the layer that lets the company keep scaling without turning the graph, the UI, or the workflows into separate systems.

The work is concrete: source ingestion, knowledge modeling, search quality, workflow state, and natural-language research.

• You want a narrow feature lane.
• You need fully specified tickets before you can start.
• You are uncomfortable owning both backend logic and product behavior.
• You prefer systems with clean inputs and static requirements.
• You do not want to work on data-heavy, search-heavy, or AI-assisted product surfaces.

• Base salary: $180K–$220K
• Equity: competitive, top decile
• Location: New York, NY
• Work model: hybrid, 3 days in office
• Employment: full-time
• Visa support: can sponsor H1B, not O-1

• Initial review: 20–30 minutes
• Technical screen — debugging: 45 minutes
• Technical screen — system design: 45 minutes
• Live jam session: 120 minutes
• Team conversations: final fit and scope discussion
• References: final step

Aurora helps exceptional engineers find the right role at some of the most ambitious startups worldwide.

We work with teams that value high ownership, strong technical standards, and clear scope.