Software Engineering PMTS - ML Engineer

About Salesforce

About the Team

Digital Enterprise Technology (DET) at Salesforce builds the internal engines that power our business. The Machine Learning & Innovation (MLI) team is the applied science and infrastructure strike-force responsible for "Foundry," our unified ML/AI Platform. We operate as "Customer Zero," building the high-velocity, real-time streaming feature platforms and recommendation models that power internal products like the Events App and enterprise-wide personalization.

Role Overview

As a Senior Staff ML Engineer focusing on statistical modeling, you will be the organization's primary technical authority on experimental methodology and causal analysis. You will build the analytical engines that allow Salesforce to scientifically measure the impact of algorithmic changes and product features, ensuring that enterprise experimentation is grounded in unshakeable mathematical rigor.

What You'll Do

• Design and implement advanced statistical methodologies for the enterprise Stats Engine, including Heterogeneous Treatment Effects (HTE) and stratified sampling.
• Lead causal inference research to determine the true incremental impact of machine learning models and next-best-action interventions.
• Develop and modularize statistical codebases to support large-scale A/B testing and experimentation platforms (e.g., GrowthBook).
• Collaborate on intent classification and predictive modeling within the Next Best Offer (NBO) discovery initiative.
• Provide expert guidance to product and engineering teams on experimental design, metric definition, and avoiding common statistical pitfalls (e.g., false discovery rates).
• Translate complex statistical results into actionable insights for technical and non-technical stakeholders.

What You Bring

• 25+ years of professional experience in data science, statistical computing, and software engineering.
• Deep, authoritative expertise in experimental design (A/B testing), causal inference, and statistical significance testing.
• Strong programming skills in Python (or R) and experience building production-grade statistical analysis libraries.
• Experience integrating statistical models with experimentation platforms (GrowthBook, Optimizely, or internal systems).
• A rigorous, methodical approach to data analysis and a commitment to scientific integrity in business decision-making.
• Exceptional communication skills, with the ability to explain complex statistical concepts clearly and concisely.