Senior Test Engineer - Metrics Quality

Every decision Avride makes about its autonomous driving software  merge or revert, ship or hold, this approach or that one — rests on a metric computed over a set of recorded scenes. Our QA organization is what stands between a metric and a decision made on a metric that was quietly wrong.

You will own the quality of the metrics themselves. Our data scientists build them; you decide whether they can be trusted, before anyone starts making release decisions with them.

This is a specific and underrated craft. A metric can be computed correctly, pass every unit test, and still be the wrong number for the decision it is meant to support. Finding that takes reading the implementation, understanding the product, and knowing what the people who rely on the number are actually trying to learn from it.

The hard half of the job is not checking that a metric fires when it should. It is finding the places where it stays silent and should not have — the failures that produce no number at all, and therefore no complaint, until someone ships on the strength of them.

• Own the acceptance of new and changed metrics. Before a metric is used to decide whether a change is safe to ship, you decide whether it can be: what it should count, what it should not, and whether the implementation agrees with either.
• Build the case space. For each metric, work out the full set of situations it has to handle — including all the ones where it must stay silent — and keep that set current as the technology and the operating environment change.
• Hunt the silent failures. A metric that returns a surprising number gets noticed. A metric that returns nothing where it should have returned something does not, and that is the class of defect you are here to find.
• Read the implementation against the case space. Not for code quality — for the real situations it does not handle, and will therefore never report.
• Build the test data the job needs. The situations a metric has to handle are rarely all sitting in the data already. Get them by whatever route is cheapest for the case at hand, and keep looking for better routes than the ones we use today.
• Make the case for a fix. Take findings to the engineer who owns the metric with concrete examples and a sense of scale: how often it is wrong, and what decisions that changes.
• Think like the people who read the numbers. A metric that is right on every individual scene can still fail to reveal a degradation across the whole set. Say so before anyone builds a release gate on it.
• Own the release cycle for metrics. Metrics ship on their own cadence. You qualify each release, run and maintain the regression that catches silent changes in what a metric means rather than only in what it returns, and make that cycle faster and cheaper as the number of metrics grows.

• 6+ years in software testing or test engineering, with real depth in data-heavy or analytical systems.
• Strong Python and SQL. You write analysis scripts and independent reference computations as a matter of routine, not as an exception.
• Statistical literacy. Distributions, variance, sample size, aggregation traps, and the confidence to say "this difference is noise".
• Product thinking. You ask what a number is for before you test it. Testing against the letter of a specification is the starting point of this job, not the substance of it.
• Conviction that survives a "that's how it works." You will regularly be the one telling a colleague that their metric does not behave the way they expect. That is a normal part of the work here, and it goes well when you bring evidence and see the conversation through rather than letting the finding quietly drop.
• Fluent use of LLMs as a working tool — analysis, test-data generation, reading unfamiliar code, and cross-checking your own reasoning.
• Clear written English. Your findings are read by people who will act on them.

• Autonomous vehicles, robotics, or evaluation of machine learning systems.
• ClickHouse or a similar analytical database.
• Experience testing evaluation pipelines or A/B testing infrastructure.
• Experience designing annotation tasks for human reviewers, and checking that their answers agree.
• Knowledge of US road rules and driver behaviour.

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