Description
Salesforce is the global leader in Cloud-based (SaaS) solutions that enable the world’s premier brands to maintain a robust online presence with high ROI. We do this by providing a highly scalable, integrated cloud platform that allows our clients to rapidly launch and manage e-commerce stores, initiate unique marketing campaigns, build service agents, and drive customer traffic across a global footprint.
We are looking for a top-notch AI scientist to join our applied science team. The team is tasked with building, optimizing, and innovating across a number of product lines including agentic enterprise search, commerce cloud, and salesforce personalization. We produce performant machine learning models, architect components to improve our agents, build benchmarks, and tune prompts to enhance relevance, personalization, and efficiency. Our goal is to deliver better shopper experiences as well as enterprise user experiences. We work on search agents, retrieval strategies, product recommendations, content and promotion selection, next-best-action, and much more.
In this role, you will utilize frontier foundational models as well as open-source models for real-world use cases. You will research, design, and prototype in close collaboration with engineering and product teams, learning from customer deployments. This is a collaborative, agile team that values technical ownership and customer value. We are looking for someone who gets a kick out of staying on top of the latest AI literature, tools, and techniques, and figuring out the best way to apply them to practical solutions. We encourage contribution to tools and knowledge sharing within the team, the company, and the industry.
Responsibilities
Work with product management and leadership to translate business problems into applied science problems.
Research, prototype, and build demonstrations of AI ideas for quick validation and feedback.
Design and build AI systems to hit accuracy and performance metric targets.
Productize AI innovations in collaboration with engineering teams.
Educate engineering and product teams about AI science during collaboration.
Actively collaborate with and provide technical guidance to other members of the team.
Share technical innovations with the team and across the company.
Create innovations and contribute to conferences and/or open-source tools.
Required Qualifications
Fluent in prototyping AI solutions, testing machine learning (ML) models, and wrangling small to medium datasets (e.g., data cleansing, feature engineering).
BS/MS in a quantitative discipline with 2+ years of relevant experience.
Expertise in classical machine learning, deep learning, and Large Language Models (LLMs).
Avid experimenter in ML, LLMs, and agent systems, with a focus on innovating current AI solutions.
Proficient in using Python scientific stack (e.g. Numpy, Pandas, PyTorch, SciPy, Jupyter).
Proficient in shell scripting, Unix/Linux command-line tools, working with cloud infrastructure (AWS).
Great communication skills: ability to discuss with scientists, engineers, designers, and product managers.
Preferred Qualifications
Ph.D. with 1+ years of relevant experience.
Deep knowledge of Reinforcement Learning, Deep Learning, Causal Inference, and Information Retrieval.
Experience in building and shipping machine learning models and agent systems to production.
Practical experience with MLOps best practices, including model serving, monitoring, and A/B testing in a production environment.
Open-source machine learning code, project contributions, or implementations of published papers.
Publications in any of {cvpr, iccv, eccv, neurips, iclr, icml, acl, emnlp, recsys, kdd}.