26 Signals

Continuous AI Evaluation Pipeline Platform

A SaaS platform that automates regression testing, human feedback collection, and quality metrics for AI systems before and after deployment.

Added May 26, 2026

AI Infrastructure

ML Operations

LLM Observability

Opportunity score

Medium opportunity (53%)

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The Problem

AI teams are repeatedly hiring engineers to build custom evaluation pipelines because model and prompt changes can regress quality in ways standard tests do not catch. They need to measure assistant quality across real user queries, subjective human judgments, and data-level model performance without stitching together fragile internal tooling.

Potential Solution

The product provides hosted evaluation suites for LLM and ML systems, combining automated benchmark runs, regression detection, structured human review workflows, and data-centric quality metrics. Teams can connect production traces, prompts, model versions, and labeled datasets, then compare changes over time and identify which data or model behaviors are driving quality shifts.

Why Now?

Companies are moving AI systems from experiments into production, creating a need for continuous evaluation infrastructure that measures real-world quality rather than one-off benchmark scores. Multiple companies are explicitly hiring for eval pipelines, observability, human evaluations, and quality metrics, indicating a buyable tooling gap.

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Showing 1-20 of 26 signals

Job adsAug 27, 2026

vinova-pte-ltd-201009399g

Full Stack AI Engineer

Build evaluation pipelines using golden datasets, regression tests, deterministic evaluators, and LLM-as-a-judge to ensure AI system quality Implement observability, monitoring, guardrails, fallbacks, and validation mechanisms to maintain reliable AI systems balancing model quality, latency, reliability, maintainability, and cost

Job adsAug 12, 2026

solstice

Member of Technical Staff (QA Engineer - Agentic Systems)

Build our evaluation systems. Because we can't check an output against a single correct answer, you'll design the evals that score quality instead and decide, with evidence, what is good enough to ship. Make models and prompt changes safe. We swap models and rewrite prompts constantly. Your tooling should flag a drop in quality, a jump in cost, or a latency regression before a customer runs into it.

Job adsAug 1, 2026

scale-ai

Frontier Agents Engineer (Forward Deployed Engineering)

Deploy evaluation harnesses using offline benchmarks, online experiments, golden datasets, regression suites, and LLM-as-a-Judge to detect quality regressions before they impact customers. Implement tracing, observability, monitoring, guardrails, grounding, and safety mechanisms that enable production AI systems to operate with confidence.

PodcastsJul 24, 2026

How to Build Production-Grade Applications With AI

Machine Learning Tech Brief By HackerNoon

Grounding answers and continuously evaluating them. Most AI failures in production are answer quality failures, not infrastructure failures. Traditional tests do not capture non-deterministic behavior well. SO evaluation has to become a continuous engineering loop. OpenAI defines evals as structured tests for measuring accuracy, performance, and reliability in production environments. Microsoft Foundry supports evaluations for performance, quality, and safety before and after deployment. Amazon Bedrock supports evaluation of models, knowledge bases, and retrieval augmented systems, including computed metrics and human-based review. Together, those sources point to the same operational pattern as every prompt, model, retrieval strategy, and policy change needs measurable regression testing before release and continuous monitoring afterward.

Job adsJul 24, 2026

microsoft

Senior Software Engineer

How do you know an AI product is actually getting better, and how do you prove it, at scale, before millions of people feel the difference? We build and operate the offline evaluation platform that gates Microsoft Copilot’s quality: teams across Copilot depend on us to run their scenarios against the product, score the responses, and produce the scorecards that decide what ships. We do this at the scale of one of the world’s largest AI products, inside a strict enterprise compliance boundary. As Copilot becomes a fleet of autonomous agents, the way we measure quality has to be reinvented, and you will be one of the engineers building it.

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