Applied Scientist LMTS

Salesforce•San Francisco, CA

About The Position

About Salesforce 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. Overview of the Role Join an agile team with deep startup roots. We operate as a high-velocity 'startup-within-Salesforce,' following our recent acquisition. You’ll be managed by the same founders and engineers who built the original company, offering the autonomy of a small team backed by the global scale and trust of Salesforce. You will have the unique opportunity to build a '0 to 1' product with the founding team, while leveraging Salesforce's world-class Data Cloud and security infrastructure to drive immediate global impact at scale. Global supply chains still rely on slow, manual processes—email, spreadsheets, and fragmented data. The economy moves fast but supply chains don’t, creating an inefficiency that affects the $13T of goods shipped annually and is one of the largest untapped opportunities in modern enterprise. Agentforce Supply Chain is reimagining the supply chain with an AI-powered platform for designing, automating, and running end-to-end business processes, with seamless collaboration through familiar channels like email. For Salesforce, this represents a massive growth opportunity in the back office, with innovations that flow into the front office. Customers are clamoring for more, rapidly expanding their use cases as we enter an exhilarating growth phase. As one user put it: “I’ve been waiting for this for 20 years.” The Impact You Will Have 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.

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
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.

Nice To Haves

Experience with supply chain, manufacturing, or related problems

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.