EvalLoop AI Quality Operations
53 Signals

EvalLoop AI Quality Operations

A managed eval pipeline platform that measures AI assistant quality, catches regressions, and routes uncertain cases to structured human review.

Added May 26, 2026

AI Infrastructure
ML Evaluation
LLM Observability
Opportunity score

Medium opportunity (65%)

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The Problem

Teams building AI products struggle to know whether prompt, model, data, or training changes actually improve real user quality. Existing evaluation work is often fragmented across benchmarks, observability, human review, and data-level metrics, making regressions hard to catch before deployment.

Potential Solution

EvalLoop connects to production AI logs, builds reusable evaluation suites, tracks quality metrics across real user queries, and compares model or prompt changes before release. It also supports structured human evaluations for subjective quality judgments and highlights data-centric drivers of performance so teams can prioritize labeling or retraining work.

Why Now?

Companies are hiring specifically for AI evaluation pipelines, LLM observability, human feedback systems, and data-level ML quality metrics. As AI assistants move into production, evaluation is becoming core infrastructure rather than an occasional research task.

Market validation
Search demand

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Showing 1-20 of 53 signals

Job adsSep 14, 2026
cursor
Software Engineer, Agent Evaluation and Quality

You’ve built and operated evaluation or measurement systems, such as AI evals, experimentation, ranking/relevance, or search quality. You can turn ambiguous “quality” questions into concrete metrics, pipelines, and decisions.

Job adsSep 13, 2026
gmp-recruitment-services-s-pte-ltd-199307527d
Product Manager (AI System)

Define and evolve evaluation frameworks using offline metrics, online experiments and human feedback. Own AI product quality and delivery, including correctness, predictability, safety and user trust.

Job adsSep 2, 2026
multiverse
Senior AI Engineer - AI Transformation

Build evaluation frameworks. You build automated eval pipelines and human-in-the-loop review processes that tell the team whether its AI systems are doing what they should.

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