# Software Engineer, Evals

**Company:** [Glean](https://hotfix.jobs/companies/glean)
**Location:** Bengaluru, India
**Role:** Backend Engineering
**Experience:** 6+ years
**Skills:** Go, Python, Java, C++, Distributed Systems, Data Pipelines, Observability, Cloud-Native Infrastructure, Llm Applications, Machine Learning Infrastructure, Tracing, Data Warehouses, Workflow Orchestration
**Posted:** 2026-07-19

> Build scalable backend and infrastructure systems for AI evaluation, observability, and quality measurement. The role requires 6+ years of software engineering experience, strong programming skills, and experience with distributed systems, data pipelines, production services, or cloud-native infrastructure.

## Job Description

## Responsibilities
- Design and build large-scale evaluation pipelines measuring assistant and agent quality across thousands of real-user and synthetic workflows.
- Evaluate frontier model releases and open-source model releases, building infrastructure and quality signals to identify regressions, tradeoffs, and launch readiness.
- Build agent observability infrastructure, including trace enrichment, durable telemetry pipelines, dashboards, and debugging workflows.
- Own backend systems from architecture and design documentation through production rollout, reliability, monitoring, and iteration.
- Partner with product, machine learning, and infrastructure engineers to make evaluations a first-class part of shipping AI features.
- Connect evaluation results, customer feedback, regression analysis, and engineering workflows to concrete product improvements.
- Build systems balancing speed, reliability, enterprise security, and cost across cloud-native environments.
- Mentor engineers, raise the bar for technical design, and help shape the engineering culture of the Bangalore AI quality team.

## Requirements
- 6+ years of software engineering experience building backend systems, infrastructure, distributed systems, or data platforms.
- Strong coding skills in Go, Python, Java, C++, or similar languages, with an emphasis on reliability, scale, and well-tested components.
- Experience with distributed data pipelines, production services, observability systems, or cloud-native infrastructure.
- Analytical rigor and attention to whether metrics reflect real user experience.
- Comfort working in customer-focused, cross-functional environments.
- Strong commitment to quality in both engineering systems and AI products.

## Nice-to-haves
- Experience with LLM applications, evaluations, tracing, data warehouses, workflow orchestration, or machine-learning infrastructure.

## Compensation & Benefits
- Compensation is determined by factors including location, level, job-related knowledge, skills, and experience.
- Certain roles may be eligible for variable compensation, equity, and benefits.

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