Backend Engineer (Ruby on Rails / PostgreSQL / LLM Pipelines), Product Testing Data

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Our hiring partner is a venture-backed early-stage US startup building software and data infrastructure for product testing and compliance. Their customers are consumer brands and manufacturers who need to verify what is actually in the products they sell, prove it to regulators and marketplaces, and share it openly with their own customers.

Testing today is slow, expensive, and the results end up buried in PDFs that nobody can compare or act on. Our partner is building the layer that changes that: a platform where testing results become structured, comparable data — data that automates compliance work, and that brands can publish to differentiate themselves.

The team is small, technical, and founded by people who have built and scaled companies before. You'd be an early engineer with real ownership of a core part of the product.

This is a backend role with the weight on data. The product surface and the core pipelines already exist — this hire is about deepening them. The team's next set of bets are data problems: pulling in messy external data at volume, organizing it so it means the same thing across sources, and turning the resulting database into something customers can act on.

The stack is Rails end to end and there's an established design system, so front-end work here is light and mostly mechanical. You may pick some of it up, or it may be handed to someone else — either way, it's not the center of this role and it's not what you'll be evaluated on.

1. External data ingestion

Customers arrive with thousands of historical test reports from external labs — all PDFs, every lab with its own format, none of it structured. There's already a working ingestion pipeline; a good part of this role is making it substantially better.

• Improve and rework the extraction pipeline that turns those documents into structured records (document AI + LLM extraction)

• Strengthen the validation and correction layer around it. Extraction that is 90% right isn't good enough when the output feeds compliance decisions — how errors get caught, surfaced, and corrected is as much of the job as the extraction itself

• Build the taxonomy that makes results from different sources comparable at all: unit normalization, naming across labs, method equivalence, detection limits, and the edge cases in how each source reports the same measurement

• Enable cohort analysis on top of that — comparing across suppliers, products, and categories once the data finally lines up

2. Turning the results database into something customers can act on

The platform holds a large and growing body of test results. Making that legible and useful to customers is the second half of the role.

• Organize and query results across time and across sources: trends, shifts, out-of-spec risk, variance by supplier or batch

• Make sure what reaches a customer is statistically defendable — the bar is knowing when there's enough signal for a customer to act on something, and being honest when there isn't

• Build agentic workflows on top of that data. The interesting engineering here isn't calling a model — it's designing what it can touch, how outputs get verified, and how conclusions stay auditable

Data modeling across audiences

As the product's scope grows, the team keeps hitting the same wall: different audiences — customers, internal ops, external partners, compliance — look at the same underlying record and need a different shape of it. Designing a model that serves all of them without forking is a real, ongoing part of this job.

• Ruby on Rails — the core application, and the center of their ecosystem

• PostgreSQL — transactional data model

• ClickHouse — one of their data warehouses, already in production for analytical workloads

• Claude API — extraction and agentic workflows

• Reducto — document parsing / PDF ingestion, already in use

• Turbo (Hotwire) + Tailwind + an established design system — front end, when it comes up

• Experienced backend engineer (4+ YOE) with strong proficiency in Ruby on Rails and PostgreSQL. Rails is strongly preferred given the existing ecosystem, though a genuinely exceptional backend engineer from another stack is worth a conversation

• Real experience with data modeling — you've designed schemas that had to survive changing product requirements, and you can walk through the trade-offs you made and what you'd do differently

• Experience building or maintaining data ingestion pipelines from messy external sources: documents, third-party feeds, vendor exports, anything you don't control the format of

• Analytical rigor. You don't need to be a data scientist or a statistician. The bar is that you reason well with data: you can look at a result and say whether there's enough there for a customer to act on, and you're comfortable saying "not yet" when there isn't. Think strong data analyst instinct, applied by an engineer

• Comfort building on LLM / agentic APIs in production: pipeline design, evaluation, and handling non-deterministic output responsibly

• Experience with analytical data stores (ClickHouse, BigQuery, Snowflake, DuckDB or similar) — strong plus

• Familiarity with Turbo (Hotwire) and Tailwind — nice to have, not a filter

• Bias to action — this is early stage, 0 to 1 execution

• Excellent communication, comfortable working autonomously and seeing the big picture

• Previous experience as a developer in high-growth startups

• Mission alignment matters here: this team cares about transparency in what people buy and consume, and they hire for people who care about it too

First Principles

• Deal with ambiguity, deconstruct the problem, build the optimal solution

• Question every requirement → delete any part or process you can → simplify and optimize → accelerate cycle time → automate

Standard of Excellence

• High standards are contagious. A+ talent attracts A+ talent

• There's a glut of mediocrity in the world. They're chasing the products and the people that strive for excellence

Bias for Action

• Take initiative, decide quickly, experiment, and learn from failures

• Customer expectations rise over time, which means improving every single day

Low Ego

• Coachability. It's not about being right, it's about getting to the right answer

• Reacts calmly to criticism, and treats feedback as information rather than a threat

• Respectfully challenges decisions you disagree with, even when it's uncomfortable — and once a decision is made, commits fully and moves forward