{"id":2280749,"url":"https://alion.io/job/quickbase-staff-software-engineer-ai-powered-automation-pipelines","title":"Staff Software Engineer, AI-Powered Automation – Pipelines","company":{"id":177983,"name":"Quickbase","domain":"quickbase.com","url":"https://alion.io/company/quickbase","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Bulgaria"],"countries":["BG"],"hiring_countries":["BG"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":64000,"max_usd":135000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2065},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Context Engineering","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"A/B Testing","optional":true},{"name":"A2A","optional":true},{"name":"AI Agents","optional":true},{"name":"BigQuery","optional":true},{"name":"Claude","optional":true},{"name":"Claude Code","optional":true},{"name":"Docker","optional":true},{"name":"Emotion","optional":true},{"name":"FastAPI","optional":true},{"name":"Flask","optional":true},{"name":"GCP","optional":true},{"name":"Gemini","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Google ADK","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Google Bigtable","optional":true},{"name":"Google Cloud Run","optional":true},{"name":"Google Workspace","optional":true},{"name":"JavaScript","optional":true},{"name":"Jenkins","optional":true},{"name":"Jest","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Platform Engineering","optional":true},{"name":"Python","optional":true},{"name":"React.js","optional":true},{"name":"Redis","optional":true},{"name":"Redux","optional":true},{"name":"Rest API","optional":true},{"name":"Slack","optional":true},{"name":"Snowflake","optional":true},{"name":"TeamCity","optional":true},{"name":"Terraform","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-09-17T15:16:48Z","employer_posted_date":null,"last_verified_at":"2026-09-17T15:16:48Z","board_verified":false,"closed_at":null,"days_open":25,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":25},"description":"Job Title: Staff Software Engineer, AI-Powered Automation – Pipelines\n\nLocation: Remote, Bulgaria\n\nDepartment: Engineering\n\nReports to: Sr Manager, Software Engineering\n\nAbout the Role\n\nQuickbase Pipelines helps customers automate complex business processes by connecting applications, data, and teams. We are evolving Pipelines into an AI-powered automation platform where agents can help users design, build, understand, and improve sophisticated automations – and where AI can make each automation more capable.\n\nAs a Staff Software Engineer on the Pipelines team, you will play a leading role in shaping the future of AI-powered automation at Quickbase. You will build agents that understand customer intent, work with integrations and data, and generate reliable automations. You will also embed AI directly into running pipelines, enabling automations to interpret information, make context-aware decisions, and handle work that deterministic rules alone cannot address. This role is hands-on and highly collaborative, with room to shape both the technical direction and the product experience.\n\nKey Responsibilities\n\nDesign and build agents that help users create, configure, troubleshoot, and improve Pipelines automations.\n\nDevelop AI-powered capabilities embedded directly within automations, such as extracting information, generating content, classifying inputs, and making context-aware decisions.\n\nLead the architecture for combining probabilistic AI behavior with reliable enterprise automation, including guardrails, permissions, observability, and failure recovery.\n\nBuild and ship fullstack product capabilities end to end, from exploration and prototyping through production rollout, evaluation, and iteration.\n\nProvide technical leadership within Pipelines and across partner teams by driving architectural decisions, raising engineering standards, mentoring engineers, and collaborating with product, design, data science, and engineering partners.\n\nWhat We’re Looking For\n\nSignificant software engineering experience, including technical leadership on complex production systems.\n\nDemonstrated ability to take ambiguous product opportunities from exploration and prototype through architecture, delivery, and production ownership.\n\nStrong backend engineering skills, with the ability to contribute across the full product stack.\n\nHands-on experience building LLM-based applications involving agents, tool use, retrieval, structured outputs, or multi-step reasoning.\n\nPractical experience running AI-powered features in production, including prompt and context engineering, evaluation, monitoring, cost control, and failure handling.\n\nStrong architectural judgment, pragmatic decision-making about when AI is the right solution, and the ability to influence technical direction across teams.\n\nNice to Have\n\nExperience building agents that plan, call tools, maintain context, and operate within defined permissions.\n\nExperience with workflow orchestration, durable execution, event-driven systems, or integration platforms.\n\nFamiliarity with evaluation approaches for AI systems, including offline evals, regression testing, human review, online metrics, or A/B testing.\n\nExperience building enterprise software with authentication, authorization, governance, auditability, and sensitive customer data.\n\nOur Tech Stack\n\nYou’ll work with a modern, production-scale platform that supports complex automations, integrations, and AI-powered experiences.\n\nBackend: Python 3.14, Flask, and FastAPI for the services behind Quickbase Pipelines.\n\nCloud platform: Google Cloud Pub/Sub, Cloud Tasks, Cloud Scheduler, App Engine, Cloud Run, and Cloud Functions for event-driven architecture and execution.\n\nData: Cloud Datastore and Bigtable as primary datastores, with BigQuery, Cloud Storage, and Redis/Valkey alongside.\n\nPlatform engineering: Docker, Terraform, TeamCity, GitHub Actions, Jenkins, Rollbar, OpenTelemetry/Cloud Trace, and Cloud Monitoring.\n\nIntegrations: 50+ channel integrations, including Quickbase, Salesforce, Slack, NetSuite, Snowflake, Microsoft 365, and Google Workspace, built on our own framework using REST APIs, OAuth, webhooks, polling, and Jinja2.\n\nFrontend: React 18, Redux/redux-saga, Emotion, and Jest on the web, in a Yarn workspaces monorepo.\n\nAI tooling: Vertex AI with Gemini and Claude, Google ADK and A2A for AI agents, and Claude Code with team-shared rules, skills, and MCP servers.\n\nBenefits\n\nUnlimited remote work policy\n\n25 days of annual leave, 2 additional days off for volunteering\n\nCompetitive remuneration package incl. an annual bonus\n\nTop-notch IT setup.\n\nMental health support, life insurance, food vouchers\n\nAdditional health insurance – for you and your loved ones\n\nAnnual wellness support allowance\n\nExternal Professional Learning 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