The Role
We’re looking for a Staff AI Engineer who thrives at the intersection of software engineering, data, and AI. This role is ideal for engineers who have experience building real, lovable products leveraging state-of-the-art techniques and modern technologies. You’ll own the full lifecycle of AI applications: building data pipelines, prompt & feature engineering, integrating models into high throughput, low latency services, and deploying production-ready, AI-powered features that delight our 70+ million users.
What You’ll Own
- Build, deploy, and maintain AI agents and features across Linktree’s core product surfaces — including personalisation, recommendations, and creator analytics.
- Partner with Product and Engineering to translate business problems into solutions, driving the roadmap from problem definition through to production.
- Design and own scalable data pipelines for feature engineering, model training, evaluation, and real-time inference.
- Establish and uphold engineering best practices for AI: evaluation design, prompt management & versioning, testing & monitoring.
- Lead technical design reviews and mentor engineers on AI foundations and production-grade AI patterns.
- Evaluate and adopt new AI techniques, frameworks, and tooling to keep Linktree at the frontier of applied AI.
Who We’re Looking For
- 6+ years engineering experience with experience in building and maintaining data intensive, production systems.
- Familiarity with modern AI engineering techniques such as prompt-engineering, fine-tuning, eval loops, and technologies such as Gemini, Claude and OpenAI APIs.
- Hands-on experience with AI tools/frameworks (e.g., Braintrust, AutoEval) and/or MLOps tools (e.g., Airflow, MLflow, Vertex AI, or similar).
- Solid understanding of data engineering practices, including ETL, batch/streaming pipelines, and data quality monitoring.
- Familiarity with cloud infrastructure (we use AWS) and containerization.
- You have experience with shortening “build-measure-learn” by means of prototyping and experimentation to iterate quickly and build software users love.