51 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 (69%)

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

Trend snapshot pending

Competition (0)

No matched competitors yet

Showing 1-20 of 51 signals

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.

Job adsSep 2, 2026

Marketing Technology Manager, AI

Design and implement AI evaluation frameworks, including model performance benchmarking, prompt evaluation, and quality assurance processes to ensure AI agents and LLM-driven outputs meet production-quality standards

Job adsSep 1, 2026

tripadvisor

AI Quality Assurance Program Manager

Audit AI Outputs & Traveler Interactions: Continuously evaluate AI-generated outputs (e.g., automated support chats, tour recommendation accuracy, and booking updates) for intent fulfillment, safety, tone, and hallucination prevention. Drive Business Updates & Insights: Deliver executive-ready reporting that translates complex QA evaluation data, model accuracy rates, and error trends into strategic recommendations for business leaders.

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 12, 2026

ambience-healthcare

Staff ML Engineer, Frontier AI

Define the AI Quality and Evaluation Strategy: Establish how we measure production AI quality across LLM and agentic systems, including automated graders, regression testing, human evaluation, failure taxonomies, and offline-to-online validation.

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