Principal Applied Scientist, Agentforce Operations

Salesforce•New York City, WA

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Join a collaborative, diverse team of researchers at Agentforce Operations. The Foundational Models Team develops the next generation of business intelligence and bridges the gap between cutting-edge research and customer value. Following our recent acquisition, we offer the agility of an early-stage startup backed by the global scale and trust of Salesforce.

Requirements

Master's or PhD in Computer Science, Mathematics, or a highly quantitative field with an AI/ML research focus
Experience developing, deploying, and evaluating machine learning models in production or top-tier research environments
Deep understanding of transformer architectures and advanced retrieval mechanisms.
Enjoy designing novel, non-standard AI solutions by introducing core architectural changes or applying foundational mathematical disciplines such as stochastic modeling, graph theory, or optimization algorithms, and have a track record of peer-reviewed publications demonstrating this work.
Production-grade Python skills, including experience with automatic differentiation frameworks like PyTorch, JAX, or TensorFlow
Experience training large-scale models across distributed GPU clusters using frameworks like DeepSpeed or TorchTitan.

Nice To Haves

Experience building production-grade ML pipelines using tools like Kubeflow, Airflow, or MLflow for tracking, versioning, and automated retraining.
Experience building robust, distributed data pipelines for cleaning and analyzing massive datasets using tools like PySpark.

Responsibilities

Design, implement, and train novel deep learning models on large-scale GPU clusters.
Prototype new architectures and algorithms for enterprise data, such as tabular, relational, and graph-structured data.
Stay current with AI research, apply relevant breakthroughs, and advise on internal AI strategy — wearing multiple hats as part of a startup-style team that values experimental rigor and shipping well-engineered systems.
Partner with engineering, product, and design teams to turn research into functional, production-ready features that create immediate, tangible customer value.