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# Software Engineer, Quality & Automation

AI Summary Fieldguide is hiring a software engineer to own quality engineering and automation for enterprise SaaS and PaaS deployments. The role combines hands-on production coding, automated testing, CI quality gates, environment-parity work, and direct collaboration with enterprise customers. A major focus is building agentic workflows that triage, reproduce, route, and help resolve defects with minimal manual intervention. The hire will analyze quality trends, reduce test flakiness, run repeatable bug bashes, and connect customer-reported issues to preventive tests or environment checks. Senior and staff-level candidates may also establish quality strategy, release standards, and engineering-wide automation practices.[Apply for this job](#job-application)[View company](https://jobicy.com/company/fieldguide.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 130k–240k / yrDepartment[QA & Testing](https://jobicy.com/categories/qa-testing.md)EmploymentFull TimeExperienceSeniorPublished3 Nov 2026Apply before3 Dec 2026Listing views33Application actions2Application toolkit

## Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

## About this role.

### About Us

Fieldguide is establishing a new state of trust for global commerce and capital markets by automating and streamlining the work of assurance and audit practitioners — specifically in cybersecurity, privacy, and financial audits. We build software for the people who enable trust between businesses.

We’re based in San Francisco, CA, and we’re backed by Goldman Sachs Alternatives, Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, and more. Over 50 of the top 100 accounting and consulting firms trust Fieldguide to power mission-critical work.

### Role Summary

This role owns quality and automation for large enterprise deployments. You’ll work closely with customers and our internal engineering team to develop a world-class QA Function that’s built from an agent-first lens.

We’re building our quality and automation capacity around agentic tooling, and this role sits at the center of that: using agents to take on the repetitive work of finding, reproducing, and resolving issues, strengthening our internal testing, and keeping our SaaS and PaaS environments consistent. You’ll also work directly with the customer on how issues get reported and resolved, and own the reporting that shows where quality is heading.

We’re hiring across all levels. Depending on your experience and what you’re looking to own, you may join executing against this scope, owning the customer’s quality experience end to end, or setting quality and automation standards across Engineering. We’ll calibrate seniority during interviews based on scope and demonstrated experience.

### What You’ll Own

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Keep environments consistent across our SaaS and PaaS deployments, so behavior a customer sees in one place matches everywhere else and configuration drift doesn’t become a defect

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Build our automation capacity for handling bugs using agentic tooling including triage, reproduction, and routing that runs without a human in the middle

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Use agentic tools to resolve bugs yourself, shipping fixes rather than filing tickets and waiting

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Run bug bashes on new features before they’re released, and make the practice repeatable enough that other teams adopt it

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Own bug reporting and communication: severity-weighted trends, hotspots, and a clear read on where quality is heading that engineering leadership and the customer both trust

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Work closely with the customer to integrate deeply with their processes and improve how they report issues, validate fixes, and decide something is ready for customers

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Improve our internal automated testing: coverage where it pays, CI signal people believe, and less flake

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Close the loop from a customer-reported issue back to the test or environment check that would have caught it

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Support our evaluation efforts by creating and curating relevant datasets that can support agent quality

### Who You Are (All Levels)

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An engineer who fixes what you find. You’re strong in the codebase and you’d rather ship the fix than hand it off

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AI-native in how you work: you treat agentic tooling as core leverage for the unglamorous parts of quality, and you’ve used it on real work rather than just tried it

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You think in trends, hotspots, and severity, not anecdotes, and you can tell a clear story from a messy bug backlog

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Strong with customers: you can sit with a frustrated enterprise user, understand their process, and be the person they trust to get it resolved

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Comfortable across the stack and across environments spanning application code, CI, and the deployment differences that make “works on SaaS” not mean “works everywhere”

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Genuinely collaborative across the organization: you’ll work daily with engineers, PM, support, and customer-facing teams, and treat that as the best part of the job

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High ownership and comfortable with ambiguity. Nobody hands you the list of what to test or what to automate first

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Able to internalize a hard domain fast. You don’t need to know audit, but you’ll learn enough to know what “broken” means to an auditor

### Higher-Level Responsibilities

At the Senior level, you may:

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Own the customer’s quality experience end to end, including the reporting and communication relationship

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Set the agentic automation strategy for bug handling and decide where automation pays and where it doesn’t

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Define what environment parity means across SaaS and PaaS and drive the work to hold it

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Collaborate with PMs and engineering leads to shape release practice and quality gates

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Mentor other engineers and raise the bar on testing rigor across teams

At the Staff level, you may:

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Set and champion quality and automation standards across Engineering, beyond this one deployment

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Drive company-level initiatives on release confidence, environment consistency, and defect prevention

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Partner with engineering and product leadership to define long-term quality strategy

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Serve as a trusted advisor to leaders across Engineering, Product, and Support

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Represent Fieldguide externally through writing, speaking, and open-source contributions

### Experience

Must-have:

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Strong production software engineering experience — you write and ship code, not only tests

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Hands-on experience building test automation and CI quality gates that engineers actually rely on

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Real use of AI or agentic tooling as part of how you work

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Experience working directly with customers or enterprise users on issues they care about

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Comfort operating in ambiguity and owning outcomes without a defined queue

Nice-to-have:

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Python, TypeScript, React, Postgres, Hasura, GraphQL

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E2E and integration frameworks such as Playwright or Cypress

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Experience with both multi-tenant SaaS and single-tenant or self-managed enterprise deployments

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Infrastructure exposure for environment parity work — AWS, Kubernetes, Terraform

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Observability and tracing tooling such as Datadog

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Experience building agentic workflows or internal tooling with LLMs

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Performance and load testing, especially on long-running workflows

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Security and compliance environments (SOC 2, FedRAMP, or similar)

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Background in audit, accounting, financial services, or another regulated domain

### Benefits

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Competitive compensation with equity

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Comprehensive health and wellness benefits

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Flexible time off and work schedules

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Technology reimbursements

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401(k) plan

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Twice-yearly in-person offsites across the U.S.

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Wellness benefits starting on your first day

### Our Values

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Fearless — Inspire and break down seemingly impossible walls

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Fast — Launch fast with excellence; iterate to perfection

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Lovable — Deliver happiness and 11-star experiences

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Owners — Execute and run the business with ownership

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Win-win — Create mutual value and earn trust for life

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Inclusive — Scale the best ideas with inclusive teams

Show more

[Apply now >](https://jobicy.com/jobs/158677-software-engineer-quality-automation.md)

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![Upload CV](data:image/svg+xml;base64,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)

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## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThis is a high-ownership role requiring strong software engineering depth in addition to test automation, CI, deployment consistency, and agentic tooling. The engineer must independently prioritize ambiguous quality problems while communicating credibly with both customers and engineering leadership.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive$185,000US market range (per year)$140k–$230k0$253.00k

AI insightThe employer discloses a yearly base-compensation range of $130,000 to $240,000 USD. The broad range appears consistent with hiring across multiple seniority levels; the estimated midpoint is $185,000 per year. Estimated US market compensation for a software quality and automation engineer with production engineering and AI-tooling responsibilities is $140,000–$230,000 annually.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Software engineering](https://jobicy.com/jobs?search_keywords=Software%20engineering.md)[Quality assurance](https://jobicy.com/jobs?search_keywords=Quality%20assurance.md)[Test automation](https://jobicy.com/jobs?search_keywords=Test%20automation.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Agentic AI tooling](https://jobicy.com/jobs?search_keywords=Agentic%20AI%20tooling.md)[Bug triage](https://jobicy.com/jobs?search_keywords=Bug%20triage.md)[End-to-end testing](https://jobicy.com/jobs?search_keywords=End-to-end%20testing.md)[Integration testing](https://jobicy.com/jobs?search_keywords=Integration%20testing.md)[Playwright](https://jobicy.com/jobs?search_keywords=Playwright.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[TypeScript](https://jobicy.com/jobs?search_keywords=TypeScript.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[Customer communication](https://jobicy.com/jobs?search_keywords=Customer%20communication.md)

Sample interview questionsHow would you investigate a customer-reported issue that cannot initially be reproduced internally?I would first collect a precise timeline, affected tenant or environment details, inputs, expected versus actual behavior, and relevant logs or traces. I would compare configuration and deployment versions across environments, create a minimal reproduction, and use observability data to validate hypotheses. Once resolved, I would add an automated regression test or environment check so the same issue is detected earlier.

How have you improved trust in an unreliable CI test suite?

I start by measuring failure categories and flake rates rather than treating all failures equally. I prioritize the highest-impact flaky tests, remove timing and shared-state dependencies, improve test isolation, and make failures diagnosable with logs and artifacts. I then establish quality gates that are strict for trusted signals while routing known nondeterministic failures for remediation.

Where would you use agentic tooling in a quality-engineering workflow, and where would you retain human oversight?

Agentic tooling is valuable for repetitive work such as clustering duplicate reports, gathering diagnostic context, proposing reproduction steps, routing issues, and drafting regression tests. Human oversight should remain for severity decisions, customer-impact tradeoffs, security-sensitive changes, and final validation of code changes. I would measure the agent's accuracy and escalation rate before expanding its autonomy.

How would you define and maintain environment parity across multi-tenant SaaS and self-managed deployments?

I would define a versioned baseline for application behavior, configuration, infrastructure dependencies, feature flags, and critical integrations. Automated checks would compare that baseline across supported environments and run targeted smoke and integration tests after deployment. Exceptions should be documented, intentional, and visible in release and quality reporting rather than becoming hidden configuration drift.

How do you communicate quality trends to engineering leaders and an enterprise customer?

I would use a concise severity-weighted view of open and resolved defects, recurrence, affected workflows, release risk, and time to resolution. I would pair the metrics with a clear narrative about hotspots, root causes, mitigation status, and decisions needed from stakeholders. For customers, I would focus on impact, workaround and resolution timelines, while keeping internal reporting detailed enough to drive prevention.

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