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Remote Ai Tester Jobs in Oregon (NOW HIRING)

This is a remote opportunity and we are looking for candidates from the U.S. The Opportunity ... testing, deployment, and ongoing operation * Build modular, composable agentic systems using ...

AI Engineer

OR · On-site +1

AI Engineer Remote, USA; potential for minimal ad hoc travel EMKS is seeking an AI Engineer to ... Collaborate with architects, engineers, testers, and business stakeholders to identify and ...

AI Engineer Temporary Assignment (through 2/13/2027) Remote, USA; potential for minimal ad hoc ... Collaborate with architects, engineers, testers, and business stakeholders to identify and ...

... testing, deployment, documentation, and early-stage production support. We are an ITAR-regulated ... Salary range is subject to location, as this role can be remote. Roles and Responsibilities

New

... testing, deployment, documentation, and early-stage production support. We are an ITAR-regulated ... Salary range is subject to location, as this role can be remote. Roles and Responsibilities

New

AI Engineer

OR · On-site +1

$155K - $180K/yr

... model testing, and automated evaluation techniques for conversational AI. You strive for ... We will also consider highly qualified remote candidates who can travel to San Francisco for in ...

Lead Engineer, AI Attack Simulation

Portland, OR · On-site +1

$108K - $143K/yr

Lead Engineer, AI Attack Simulation Remote [within the US] ABOUT THE ROLE: HiddenLayer is seeking a ... security testing capabilities. You will partner closely with Product Management and Security ...

Staff Software Engineer, Applied AI

OR · On-site +1

$200K - $260K/yr

Experience evaluating and testing LLM workflows: eval harnesses, golden datasets, regressions, and ... We are remote-first with a dedicated NYC office and reimbursement options for co-working spaces.

Enterprise Risk Analyst - AI Risk

OR · On-site +1

$68K - $127K/yr

Remote or onsite, we are committed to ensuring you are fully engaged and included in our ... Develop analyses, testing procedures, dashboards, monitoring tools, and reports that support AI ...

US-Remote or Marlton, NJ area Description A Software Engineer is needed to design, develop, and ... testing, and DevSecOps practices. * Familiarity with the AI/ML lifecycle including data preparation ...

Senior AI Automation Engineer

OR · On-site +1

$103K - $136K/yr

A/B testing agent versions, model comparisons. Programming for Automation * Write modular, reusable ... Remote

Senior Agentic AI Software Engineer

OR · On-site +1

$122K - $161K/yr

United States - Remote Clearance: Ability to obtain and maintain a Public Trust LTS is seeking a ... Implement testing, evaluation, monitoring, observability, guardrails, and LLMOps practices to ...

Engineering Manager, AI Intake

OR · On-site +1

$220K - $275K/yr

Experience with automated evaluation and testing of LLM powered workflows * Experience with ... We are remote-first with a dedicated NYC office and reimbursement options for co-working spaces.

Senior Forward Deployed Engineer (AI Agent)

OR · On-site +1

$104K - $143K/yr

Ability to use data-driven decision-making, including A/B testing and performance monitoring, to ... Remote work setup budget to help you create a productive home office * Monthly wellness and ...

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Remote Ai Tester information

What is a remote AI tester?

Remote AI Testers are professionals who evaluate and validate artificial intelligence systems, algorithms, or applications from a remote location. Their primary role is to ensure that AI models work as intended by testing for accuracy, reliability, and potential biases. They may create test cases, report bugs, and provide feedback to development teams to help improve AI products. This job often requires technical knowledge of AI, attention to detail, and strong communication skills. Working remotely allows AI testers to collaborate with teams globally and test software in various real-world environments.

What are the key skills and qualifications needed to thrive as a remote AI tester, and why are they important?

To thrive as a Remote AI Tester, you need a strong understanding of software testing principles, programming basics (such as Python), and familiarity with AI/ML concepts, often supported by a degree in computer science or related field. Experience with testing frameworks, version control systems like Git, and bug tracking tools such as Jira is typically required. Attention to detail, analytical thinking, and effective remote communication are essential soft skills for this role. These skills ensure accurate evaluation of AI systems, reliable test coverage, and seamless collaboration with distributed teams.

What are some common challenges faced by remote AI testers, and how can they be addressed?

Remote AI Testers often encounter challenges such as limited direct communication with development teams and difficulties in understanding complex AI models without in-person support. To address these, it’s important to proactively schedule regular virtual meetings, document testing procedures thoroughly, and leverage collaboration tools to share findings efficiently. Staying updated with the latest testing frameworks and maintaining a strong self-management routine can also help ensure productivity and quality while working remotely.

What is the difference between Remote Ai Tester vs Remote Data Annotator?

AspectRemote Ai TesterRemote Data Annotator
Required CredentialsBasic understanding of AI/ML concepts, sometimes certifications in testing or QAAttention to detail, training in annotation tools, no formal certifications required
Work EnvironmentRemote, often collaborative with AI development teamsRemote, focused on data labeling and annotation tasks
Industry UsageAI development, machine learning projectsData preparation for AI models, machine learning datasets
Common Search/ComparisonYesYes

Remote Ai Testers and Remote Data Annotators both work remotely in AI-related fields. While Ai Testers focus on evaluating AI models' performance and accuracy, Data Annotators prepare and label data for training AI systems. Both roles require attention to detail and familiarity with AI workflows, but Ai Testers often need a basic understanding of AI concepts, whereas Data Annotators primarily focus on data labeling tasks.

What are the most commonly searched types of Ai Tester jobs in Oregon?

The most popular types of Ai Tester jobs in Oregon are:

What cities in Oregon are hiring for Remote Ai Tester jobs?

Cities in Oregon with the most Remote Ai Tester job openings:

Staff AI Engineer | US | Remote

Grafana Labs

OR • On-site, Remote

Full-time

Re-posted 5 days ago


Key responsibilities

  • Own end-to-end development of multi-agent AI systems, including architecture, implementation, testing, deployment, and ongoing operation

  • Build modular, composable agentic systems and backend services that connect AI models to internal and third-party data platforms

  • Partner with teams to scope automation problems, design workflows, and build scalable, self-service automation solutions


Job description

This is a remote opportunity and we are looking for candidates from the U.S.

The Opportunity

Grafana Labs is seeking a Staff Engineer (AI & Automation) to own the AI agent infrastructure and automation platform that powers our Marketing Operations organization. You'll build multi-agent architectures, LLM integrations, and backend services that connect AI models to internal and third-party data platforms. You'll ship production systems that teams depend on daily.

This is a high-autonomy role where you own the technical direction. You'll identify the highest-leverage problems across Marketing, RevOps, and SDR teams, design the solutions, and ship them. You'll define the technical direction for the automation platform (data models, API contracts, shared libraries, reference architectures) and partner with Data Engineering, GTM Systems, and Field Operations to build scalable, self-service automation that eliminates manual work and drives operational efficiency.

What You'll Be Doing

Agentic Systems & AI Infrastructure

  • Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation
  • Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams
  • Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs)
  • Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management
  • Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths

Systems Integration & Backend Services

  • Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools)
  • Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context
  • Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure

Automation & Workflow Enablement

  • Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes
  • Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards
  • Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently

We invest heavily in developer productivity. You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of your choice within security guidelines). We encourage pragmatic AI-assisted development paired with strong code review and quality standards.

What Makes You a Great Fit

  • 8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering
  • 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes
  • Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline
  • Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation
  • Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring
  • You diagnose business problems before writing code. You think in workflows and outcomes, not just functions.
  • Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run)
  • Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management
  • Proven ability to identify high-leverage problems, push back on low-impact requests, and deliver end-to-end with minimal direction
  • Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code). You use AI to build AI systems
  • Clear technical communicator who can explain complex systems in simple terms to both engineers and business stakeholders

Bonus Points

  • Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector)
  • Familiarity with marketing or sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach)
  • Experience with frontend frameworks (React, Slack Block Kit) for building user-facing AI tool interfaces
  • Observability tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks)
  • Experience with workflow orchestration platforms (n8n, Temporal, Prefect, Airflow)
  • Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources
  • Prior work automating marketing, sales, or customer success workflows in a B2B SaaS environment
  • Active in open-source communities. Grafana is built on OSS and we value engineers who share that DNA

In the United States, the base compensation range for this role is USD $154,445 - USD $185,334. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes-RSUs help us stay aligned and invested as we scale globally.