73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The company's AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit.
Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures. Role Summary
We’re hiring a QA and Test Infrastructure Engineer into Platform Engineering. This is not a manual testing role and it isn’t a test-writing service for other teams. You’ll build the testing platform: the frameworks, environments, data, and pipelines that let every product team test their own work quickly and trust the result.
Our platform handles financial data that ends up in valuations, LP reports, and audit files. A regression that reaches production is expensive in a way that a bug in most SaaS products isn’t. At the same time we ship fast, and slow or flaky tests are the most reliable way to make engineers stop caring about tests altogether. Your job sits exactly in that tension.
You’ll sit alongside our Cloud Infrastructure and SRE engineers in Platform Engineering, sharing the same CI/CD estate, Kubernetes platform, and multi-cloud footprint across Azure, AWS, and GCP. You’ll own the testing half of that platform.
We’re hiring at mid-level and senior. What differs is scope and the breadth of what you’ve owned, not the standard of craft.
What You’ll Own
Test Infrastructure and Frameworks
Build and own the test frameworks and harnesses our engineers use across services written in Java, Python, and TypeScript.
Own test execution in CI: parallelisation, sharding, caching, selective test runs based on what changed, and keeping the feedback loop fast enough that engineers wait for it rather than route around it.
Own ephemeral test environments — spun up per pull request on Kubernetes, torn down after, with the dependencies a real integration test needs.
Build contract testing between services so integration failures surface at the boundary rather than in a staging environment nobody trusts.
Own end-to-end and UI test infrastructure (Playwright, Cypress, or similar) and keep it stable enough to be worth having.
Build the performance and load testing capability, so a change that degrades latency is caught before a client notices.
Test Data and Environments
Own test data as a first-class problem: realistic financial fixtures, portfolio and fund structures, multi-currency and multi-period cases, and the edge cases that break naive assumptions.
Build data generation and anonymisation tooling so teams can test against realistic data without touching real client data. Confidentiality here is not negotiable.
Manage environment parity and seeding, so a test that passes in CI means something about production.
Own the lifecycle: refresh, reset, versioning, and cleanup, so test data doesn’t quietly rot into something nobody trusts.
Quality Signals and Flakiness
Treat flakiness as a defect in the platform, not an inconvenience. Detect it, quantify it, quarantine it, and drive it down with data.
Own the quality signals engineering runs on: coverage where it’s meaningful, test suite duration, failure rates, escaped defects, and change failure rate.
Make those signals visible and useful to engineering managers and product leadership, and report honestly when the trend is going the wrong way.
Analyse escaped defects to find the gap in the testing strategy rather than the person who missed it.
Work with SRE so pre-production quality signals and production reliability data tell one coherent story.
Testing Probabilistic and AI-Enabled Features
Build the test infrastructure for AI-enabled features, where the same input can produce different valid outputs and assert-equals stops working.
Implement the techniques that do work: snapshot and tolerance-based assertions, schema and constraint validation, property-based testing, and deterministic seams around non-deterministic components.
Build the infrastructure that AI evaluation runs on — harness execution, dataset management, and CI integration — in partnership with our AI Evaluation Engineer, who owns eval methodology and scoring.
Help teams draw the line clearly between what a deterministic test should assert and what belongs in an eval suite.
Enablement and Practice
Enable teams to own their own testing. Build the tooling, set the patterns, and hand it over rather than becoming the team that tests everything.
Define and champion the testing strategy across engineering: what belongs in unit, integration, contract, end-to-end, and eval, and why the shape of that pyramid matters.
Train engineers and test engineers on testing practice, including testing probabilistic systems, which needs to be taught as its own discipline.
Review test approaches at design time and ask how a feature will be verified before it’s built, not after.
Write documentation and reference implementations people copy.
What You Bring
Hands-on engineering experience building test infrastructure, automation frameworks, or developer tooling. This is a software engineering role and you’ll be judged on the code you write.
Strong programming ability in at least one of Python, Java, or TypeScript, and comfort reading all three. Our services span them.
Real experience owning test automation in CI/CD, including the unglamorous work of making a slow, flaky suite fast and trustworthy.
Working knowledge of Kubernetes and containers, enough to build and debug ephemeral environments.
Experience with test data management, particularly where privacy or confidentiality constrains what you can use.
Demonstrated experience with contract, integration, and end-to-end testing across a distributed system, and clear judgement about what belongs at each level.
Familiarity with performance and load testing, and with reading the results properly.
Interest in testing AI-enabled features, and an understanding of why non-determinism breaks conventional assertions. Prior experience here is a strong plus rather than a requirement.
A platform mindset. You measure your work by what other engineers can do without you.
Experience in a scaleup where you had to raise the quality bar while the product and team were still growing.
Tool-agnostic judgement. We name our stack, but we’d rather hire someone who picks the right tool than someone who defends a favourite.
FinTech or financial services experience is a plus. Willingness to understand what a correct financial output looks like is not optional — you can’t design good fixtures for a domain you don’t understand.
Clear communication. You can write a testing strategy engineers will follow and explain a quality trend to leadership without over-simplifying it.
How You Work With AI
We’re an AI-first company, and that has to mean something more than having a coding assistant installed. We expect engineers here to have genuinely changed how they work.
You use AI as a working tool across the job: generating test cases and edge-case fixtures, exploring untested paths in unfamiliar code, triaging failures and clustering flaky tests, drafting test plans, and writing the documentation nobody else wants to write.
You go beyond prompt-and-paste. You’ve built or configured something (agentic workflows, automated triage or review, evaluation harnesses, custom tooling, MCP integrations) that made a repeatable part of engineering measurably faster or more reliable.
You know where AI output cannot be trusted. A generated test that asserts the current behaviour rather than the correct behaviour locks in the bug. You verify, you review, and you can explain how you verified.
You apply the same scrutiny to AI-generated tests as to any pull request: read it, challenge it, own it once you ship it.
You share what works. A technique you discovered that stays on your machine is worth a fraction of one the whole team adopts.
If your answer to “how do you use AI” is autocomplete and the occasional chat window, this role will feel like a stretch — and we’d rather tell you that now.
You Will Excel If You…
Question everything. A test suite with high coverage and no assertions, a flaky test everyone retries, a staging environment nobody believes: you raise it rather than work around it.
Believe a slow test suite is a correctness problem, because engineers stop running it.
Would rather build the tooling ten teams use than test ten teams’ features yourself.
Find flaky-test investigation satisfying rather than tedious.
Care that the numbers are right, because in our business a wrong number reaches an investment committee.
Are comfortable owning something end to end, including the pager for the platform you build.
Know the difference between pragmatic and sloppy, and won’t compromise on the former to avoid the latter.
