AI Native Software Engineer

Overview

As an AI-native software engineer on a new product portfolio, you will ship cross‑stack software with embedded AI capabilities. You’ll work in small, autonomous teams close to customers, turning ambiguous problems into high‑quality products. You will design agentic systems and guide productions from prototype to scale, balancing speed with reliability and security. Your work shapes how AI-native engineering practices evolve in a fast‑growing product organization.

Pay / Benefits

• up to 25 days’ vacation
• private medical insurance
• 3 extra days leave per year for charitable work

Responsibilities

• Build and ship end‑to‑end product capabilities across frontend, backend, APIs, data models, and AI systems
• Collaborate with product managers and designers to translate customer problems into simple, high‑quality products
• Design and implement agentic systems with model interactions, tool use, retrieval, orchestration, and guardrails
• Create and maintain the engineering context, tests, CI pipelines, and evaluation systems for AI agents
• Set architectural direction for new product areas and balance speed, simplicity, security, and reliability
• Own product and production outcomes with a focus on reliability, observability, and customer value
• Provide rigorous technical reviews and help elevate AI‑native practices across the team

Key requirements

• Minimum 3 years building production software across the full stack
• Minimum 3 years of software engineering fundamentals (system design, data modeling, APIs, testing, cloud-native development, security, observability, operations)
• Minimum 1 year of hands‑on AI-native engineering experience (LLM APIs, coding agents, tool integrations, retrieval systems, evaluations, or production guardrails)
• Minimum 1 year of experience delivering reliable AI or agentic systems from zero to production
• Bachelor’s degree or equivalent work experience (or Associate’s with at least 6 years)
• Demonstrated ability to leverage AI development tools to increase engineering leverage without sacrificing quality
• Strong communication skills with product managers, designers, customers, and domain experts
• Genuine interest in frontier AI and willingness to experiment with new models and tools

• Good judgment under ambiguity
• Strong product instincts and focus on usefulness and UX
• Clear communication of technical decisions to cross‑functional partners
• AI-native development
• LLM APIs and coding agents
• Agent frameworks and tool integrations

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