Practice Evaluation-Driven Development: Build and maintain evaluations to measure the correctness, reliability, and effectiveness of AI applications. Leverage evaluation datasets, automated testing, production observability, and user feedback to understand failure modes and continuously improve system behavior.
Iterate Closely with Stakeholders: Partner directly with analytics and business teams to understand their workflows, challenge assumptions, and translate ambiguous problems into practical solutions. Rapidly prototype, validate ideas, and refine products based on real-world feedback.
Own Software End-to-End: Take ownership across the software lifecycle, from technical discovery and architecture to implementation, testing, deployment, monitoring, and continuous improvement. Be comfortable working across data infrastructure, backend services, and user-facing applications where needed.
Balance Speed with Engineering Discipline: Leverage modern development tools, including AI coding agents, to accelerate delivery while maintaining high standards for software design, automated verification, security, and maintainability.
Contribute to Engineering Excellence: Drive technical design discussions, contribute to architectural decisions and RFCs, participate in code reviews and pair programming, and mentor other engineers through knowledge sharing and collaboration.
Maintain Production Reliability: Take responsibility for the systems you build and maintain, including observability, incident investigation, performance optimization, and participation in the team’s on-call rotation.
Build and Own Data & Context Pipelines: Design, build, and maintain the ingestion, ETL/ELT, and context-retrieval pipelines that feed our AI models, semantic layers, and agentic workflows. Take shared ownership of pipeline reliability, data freshness, and incident response as an active participant in the team's production on-call rotation
Strong Software Engineering Fundamentals: You have 7+ years of software engineering experience, with strong proficiency in Python, SQL, system design, and automated testing. You enjoy building reliable, maintainable production systems.
Applied AI Experience: You’ve developed LLM-powered applications, agentic workflows, or related systems, and understand the challenges of taking AI solutions from experimentation into production.
AI Evaluation & Observability: You’ve built evaluation datasets, frameworks, or automated harnesses to systematically measure and improve AI application performance, or have experience with observability tools such as Langfuse.
Engineering and Product Judgment: You have informed opinions about how AI systems should behave. You know when to use LLMs, when deterministic software is more appropriate, and how to balance correctness, complexity, cost, and user experience.
Curiosity and Technical Awareness: You stay informed about emerging AI research, models, agent architectures, and software engineering practices. You enjoy experimenting with new ideas while remaining pragmatic about what delivers value.
Ownership and Product Mindset: You’re comfortable navigating ambiguity, working directly with stakeholders, and making thoughtful decisions when there isn’t a single right answer. You care about the experience of the people using your software.
Pragmatic Engineering Approach: You enjoy shipping quickly to learn, but don’t compromise user trust, software reliability, or maintainability in the process. You value evidence, testing, and continuous improvement.
Collaborative Mindset: You enjoy working with other engineers through technical discussions, pair programming, code reviews, and mentorship.
Good to have
Hands-on Data Engineering Experience: You’ve built and maintained production data pipelines, data integrations, or data-intensive systems, and understand the importance of data quality, modeling, and operational reliability.
Modern Data Platforms: You’ve worked with technologies such as BigQuery, dbt, Apache Airflow, PySpark, or lake house architectures.
Knowledge & Semantic Systems: You’ve built applications integrating LLMs with structured data, semantic layers (dbtflow, cube), knowledge graphs, context engineering, or retrieval systems.
Cloud & DevOps: You have experience with AWS or GCP, Docker, and automated CI/CD pipelines.
Full-Stack Product Development: You’ve shipped product features end-to-end and are comfortable working across frontend, backend, and model integrations.
Developer Experience: You’ve built internal platforms, developer tools, or infrastructure that improve how other engineers build, test, or operate software.
What we can feed you
A collaborative, international engineering team working on challenging problems across data infrastructure and applied AI.
Opportunities to own meaningful projects end-to-end, from early ideas to production systems used by business teams.
A culture of technical learning, experimentation, knowledge sharing, and professional development.
Regular company and team events, with a commitment to diversity and inclusion.
Benefits include free food, health and dental insurance, and learning and development opportunities.
foodpanda is proud to be a company that believes in diversity and representation as key to creating not only an exciting product, but also an amazing customer and employee experience. We hire people based on merit and qualifications and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, or any other aspect that makes you, you. If you have a disability or special need that requires accommodation, please let your Talent Acquisition partner know.
To all recruitment agencies: foodpanda does not accept third-party resumes. Please do not send resumes to our jobs alias, foodpanda employees, or any other organization location. foodpanda is not responsible for any fees related to unsolicited resumes.
