Applied Scientist, Salesforce

• Masters/PhD in Computer Science or quantitative field with research in AI
• 5-7+ years of industry or post-Masters/PhD experience developing, building, and deploying Machine Learning models for real-world scenarios
• Strong experience in deep learning and graphs or network theory. Experience with at least one other form of machine learning, such as natural language processing, reinforcement learning, computer vision, LLMs, etc
• Deep expertise in training large-scale models across distributed systems using frameworks like PyTorch Distributed (DDP/FSDP), DeepSpeed, or TorchTitan on GPU clusters
• Proficiency with Python and PyTorch, Tensorflow, or JAX
• Demonstrated ability not only to use state-of-the-art machine learning techniques but also to innovate upon them
• End-to-End MLOps Proficiency: Hands-on experience building and maintaining production-grade ML pipelines using tools such as Kubeflow, Airflow, or MLflow for experiment tracking, versioning, and automated retraining

Desirable

• As a Lead Applied Scientist for the Agentforce Supply Chain Foundational model team, you will be instrumental in the development of a novel foundational model that we are building for complex tabular datasets
• Develop, implement, and deploy AI models to solve real-world complex problems in global supply chain and manufacturing
• Apply and build upon state-of-the-art machine learning technologies, stay-up-to-date with current developments in the AI field, and use your expertise to inspire AI applications across the organization
• Collaborate with cross-functional teams including Engineering, Design, Product Management, and industry experts to build high-quality product features that can be used by major companies around the world
• Establish and scale robust evaluation frameworks by creating domain-specific benchmarks and automated testing suites; implement production telemetry pipelines to monitor real-world model performance, data drift, and system health