Founding Engineer — Full-Stack, Product, Data & Applied AI

Turn listening data into something people value.

TasteID is building user-owned taste profiles from real listening behavior. We’re starting with music: helping people understand their taste and use it for discovery and personalization.

Company stage and hiring timeline

TasteID is pre-seed and pre-revenue, and we’re currently fundraising. We’re beginning candidate conversations now for a planned paid, full-time hire. The start date is contingent on securing sufficient funding; we’ll keep candidates informed as timing becomes clearer.

The role

We’re looking for a product-minded, data-fluent founding engineer. Someone who can understand a user’s problem, explore the underlying data, build the experience end to end, and learn from what happens after launch.

This is a hands-on full-stack role with real ownership of product decisions and data quality. You’ll work directly with the founders to decide what to build, what to simplify, and how to tell whether it works.

What you’ll own

• Shape the product with the founders and users. Turn feedback and ambiguous needs into focused experiments, clear priorities, and useful releases.
• Ship complete features across the interface, APIs, databases, and background processing—from onboarding and data connections to listening insights and discovery.
• Make behavioral data trustworthy. Build ingestion and transformation pipelines that handle duplicates, missing fields, inconsistent sources, retries, and changing data over time.
• Explore the data yourself using SQL and Python or comparable tools. Investigate patterns, test assumptions, and turn useful findings into maintainable product features.
• Build the learning loop. Define events and success measures, investigate where users get stuck, and combine usage data with direct feedback to choose the next improvement.
• Evaluate personalization against simple baselines. Use embeddings, retrieval, or other ML techniques when they improve the experience; examine failure cases and distinguish relevance from engagement alone.
• Make user control part of the experience. Build understandable permission and deletion flows, and communicate what the data shows versus what the system infers.
• Own quality after launch through practical tests, monitoring, debugging, documentation, and iteration.

What we’re looking for

• Evidence that you have shipped and maintained software used by real people, with a clear account of what you personally owned.
• Strong full-stack skills: TypeScript/JavaScript and React, plus backend development in Python, Node.js, or a comparable environment.
• Confidence with SQL, relational data modeling, APIs, and asynchronous processing. You can trace a problem from a screen back to the source data.
• Practical data reasoning. You question misleading metrics, recognize missing or biased samples, and can explain what an analysis does—and does not—establish.
• Product judgment. You can identify the smallest useful solution, explain a trade-off, and change direction when the evidence changes.
• Clear communication and ownership. You involve the right people, make uncertainty visible, and stay accountable after a feature ships.

We care more about what you have built and how you reason than a particular degree, employer, or title. You do not need to be a research scientist or have held a product-manager title. You do need to be comfortable working where product, software, and data meet.

Particularly relevant experience

You do not need all of these:

• Consumer products shaped by behavioral data, personalization, search, or recommendations.
• Product analytics, experiment design, cohort analysis, or evaluation of model quality.
• Turning an analytical notebook or ML prototype into a reliable user-facing feature.
• Third-party integrations, SDKs, browser extensions, or mobile applications.
• Privacy-sensitive products with meaningful user control.

Why this role

You’ll help shape what TasteID becomes, with a direct connection between the systems you build and the experience people have. The work spans consumer product, data infrastructure, and the practical challenge of making personalization genuinely useful.

An interest in music helps. Curiosity about people, care with data, and a habit of finishing things matter more.

How to apply

Send us a product, repository, or technical project you helped build. Tell us:

• Who it was for and what problem you chose to solve.
• What you personally built and one important trade-off you made.
• How you used data or user feedback to evaluate it, what you learned, and what you changed.

Measured results are welcome; an honest account of an unsuccessful experiment is useful too. If the work is private, a written walkthrough is welcome—please do not share confidential code or customer data.