Global supply chains still rely on slow, manual processes—email, spreadsheets, and fragmented data, creating an inefficiency that affects the $13T of goods shipped annually, and Salesforce is looking to solve this problem with an AI-powered platform for designing, automating, and running end-to-end business processes.
Requirements
- 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
- 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.
Responsibilities
- 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.
Other
- Masters/PhD degree in Computer Science or quantitative field
- 5-7+ years of industry or post-Masters/PhD experience
- Ability to collaborate with cross-functional teams
- Ability to work in a fast-paced environment
- Travel requirements not specified
