Staff Software Engineer — Agentic Development (AI)
Контакты
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
Germany
aitypescriptci cdgitlab ciruby on railsstartupreactdeveloper productivitypostgresqlcoding agents
Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Это узкоспециализированная и прогрессивная роль, сфокусированная на продуктивности разработки и AI-native процессах, предлагающая значительное влияние на жизненный цикл создания продукта.
Staff Software Engineer — Agentic Development (AI): Building the coding-agent harness, verification systems, CI environments, and human approval workflows that help a small engineering team safely ship software at agent speed with an accent on context engineering, testing, code review, and engineering productivity. Focus on designing reliable agent workflows, measuring rework and review effort, and reducing human cognitive load while preserving architectural judgment and quality.
Location: Munich, Germany; hybrid work, with regular in-person co-working in Berlin and Munich.
Company
hirify.global is a fast-growing, AI-native B2B logistics startup building agentic platforms that connect companies, suppliers, and carriers across European supply chains.
What you will do
Own the coding-agent harness, including context, tools, instructions, environments, and feedback loops.
Make the repository easier for coding agents and engineers to understand and change.
Build automated verification with tests, E2E checks, browser checks, static analysis, and other evidence.
Improve merge-request handoffs with clear scope, summaries, risks, and review proof.
Build reliable CI and disposable environments, plus risk-based human approval gates.
Measure agent success, rework, review effort, lead time, and escaped defects, and turn effective AI workflows into team-wide engineering capabilities.
Requirements
At least 7 years of software engineering experience, or equivalent evidence of operating at Staff level.
Production software engineering experience, including writing, reviewing, and operating production systems.
Claude Code, Codex, or equivalent coding agents must already be central to the way you work. Experience must go beyond prompting and include building harnesses, scripts, tool or MCP integrations, repository instructions, verification loops, or multi-step agent workflows.
Strong understanding of context engineering, testing, CI, code review, and human cognitive load.
Ability to review difficult merge requests and identify architectural problems, poor abstractions, and weak tests.
Experience with developer tooling, CI/CD, testing infrastructure, or engineering productivity.
Ability to work in the product codebase as well as on tooling around it.
Continuous experimentation and ability to demonstrate approaches that failed and those that worked.
Focus on increasing the output of the whole engineering organization, not only individual productivity.
Nice to have
Experience with Ruby on Rails, React, TypeScript, PostgreSQL, and GitLab CI.
Background as a Staff or Principal Product Engineer with extensive AI-native experience.
Developer Productivity or DevEx engineering experience with strong application-engineering credentials.
Experience as a founding engineer at an AI-native startup.
Experience building internal coding-agent infrastructure at a high-performing software company.
Culture & Benefits
Small, focused pods with ownership of real outcomes across product, customer solutions, growth, and brand.
AI-powered, human-led work focused on judgment, creativity, and ownership.
Continuous feedback, 360-degree reviews, a dedicated development budget, and close work with an experienced management team.
Inclusive, diverse, collaborative culture with virtual events, offsites, and regular in-person co-working.
VSOP program providing a share in company growth.
Hiring process
Bring an AI workflow and explain the most sophisticated engineering task currently delegated to an agent and what remains deliberately human.
Participate in a working session where AI tools are allowed and expected; evaluation covers delegation, context, verification, risk judgment, and reviewability.
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