
Pinterest · Posted 2d ago
Senior Staff Software Engineer (AI Platform)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 United States
- Contract
- Full-time
- Role
- ML engineering
- Source
- Stapply Data
Stack
- LLM
- Python
The posting
Join Pinterest as a Senior Staff Software Engineer on the AI Platform team. In this role, you will shape and scale Pinterest's AI/ML evaluation platform into a unified, reliable system adopted company-wide. You will identify modernization opportunities, elevate the roadmap, and execute the technical strategy for a durable evaluation system for GenAI and ML workloads. You will also define cross-team technical strategy, align partner teams, and drive broad adoption of shared evaluation capabilities. Additionally, you will make durable architectural decisions that improve the user experience and reliability of Pinterest's AI/ML evaluation platform.
- Identifying modernization opportunities and executing technical strategy for a durable evaluation system for GenAI and ML workloads. - Defining cross-team technical strategy, aligning partner teams, and driving broad adoption of shared evaluation capabilities. - Leading complex cross-team workstreams, resolving technical tradeoffs and risks, and delivering impact.
- Deep familiarity with GenAI and RecSys evaluation libraries and frameworks, AutoEvals, OpenEvals, Ragas, etc - 3+ years of deep, hands-on experience designing and operating evaluation systems for GenAI and complex ML systems, including benchmark design, golden datasets, automated metrics, human evaluation, model-based grading, and failure analysis - Strong programming skills in Python coupled with a solid grasp of distributed systems principles, and comfortable iterating with AI tools on coding and design - Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience - Strong judgment around GenAI risks, including robustness, bias, privacy, security, and the limitations of automated evaluation signals - 5+ years of experience building scalable platforms for distributed experimentation, data and model versioning, orchestration, observability, release management, and production monitoring