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.

What You'll Actually Be Doing

• 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.

You're Our Person If...

• You have a Master's or PhD in Computer Science, Mathematics, or a highly quantitative field with an AI/ML research focus
• You possess experience developing, deploying, and evaluating machine learning models in production or top-tier research environments and have a deep understanding of transformer architectures and advanced retrieval mechanisms.
• You 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.
• You have production-grade Python skills, including experience with automatic differentiation frameworks like PyTorch, JAX, or TensorFlow, and experience training large-scale models across distributed GPU clusters using frameworks like DeepSpeed or TorchTitan.

Even Better If...

• You have experience building production-grade ML pipelines using tools like Kubeflow, Airflow, or MLflow for tracking, versioning, and automated retraining.
• You've built robust, distributed data pipelines for cleaning and analyzing massive datasets using tools like PySpark.