Data Scientist II, ML Infrastructure
About The Position
This role focuses on advancing the science and systems behind ML measurement, feature understanding, and causal inference at scale. The work spans areas such as production feature importance platforms, observational causal estimation in Pytorch, large-scale proxy metric development, and data-driven approaches to ML infrastructure efficiency. We're looking for an enthusiastic individual contributor to perform high-impact technical work across this space. This person will drive foundational innovations, own the end-to-end design of production ML systems, establish rigorous methodological standards, and partner cross-functionally to turn successful research into durable platform capabilities that raise the ceiling for the entire ML organization. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.
Requirements
- 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist or software engineer, with significant ML production experience.
- Strong Python skills; experience with PyTorch or equivalent deep learning frameworks; familiarity with distributed compute (Spark, Ray). Ray specifically is a strong plus.
- Enthusiasm for building tools and platforms that multiply the impact of an entire ML organization; not just solving one-off problems.
- Deep ML theory knowledge with extremely strong fundamentals that can help us reason about ML models from first principles.
- Proficiency in software development best practices including version control, code review, and reproducible ML pipelines.
- Experience with workflow management tools (Airflow, Prefect, Jenkins, or similar) for reliable ML pipeline orchestration.
- Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.
Responsibilities
- Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB & Ray while establishing reusable patterns for other teams.
- Apply and productionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities. Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.
- Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods, unlocking step-change improvements in model quality and business outcomes.
- Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks, from feature importance to content deindexing, that improve platform efficiency and speed.
- Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale across all models.
Benefits
- The position is also eligible for equity.
- Information regarding the culture at Pinterest and benefits available for this position can be found here.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Associate degree
