Machine Learning Engineer, Salesforce

• 10+ years of industry experience in ML engineering, building AI systems and/or services
• Strong proficiency in NLP and machine learning models
• Experience with LLMs and prompt engineering
• Proven experience building and applying machine learning models to business applications
• Strong programming skills in Python, with experience in machine learning frameworks like TensorFlow or PyTorch
• Proven track record to innovate and deliver results at scale
• Experience with distributed, scalable systems and modern data storage, messaging, and processing frameworks, such as Kafka, Spark, Docker, and Hadoop
• Proven understanding of deep learning and machine learning algorithms
• Grit, drive, and a strong sense of ownership, coupled with collaboration and leadership skills

Desirable

• Salesforce is looking for a Principal Machine Learning Engineer to be part of Einstein Foundation team building the next-gen Retrieval-Augmented Generation (RAG), leveraging sophisticated generative AI services, pipelines, and components to drive the development and delivery of Agentforce
• You will work on and ship impactful generative AI platforms, applications, and products used by millions of people every day
• We are a group of data scientists, machine learning engineers, and software engineers passionate about designing data products and building intelligent agents that work alongside humans to drive customer success 24/7
• We love to learn, teach, and help each other, and we seek individuals with a collaborative attitude and openness to others' ideas
• You will play a critical role in integrating artificial intelligence and generative AI into advanced RAG technologies, enterprise Knowledge Graphs, and the latest LLM algorithms and technologies to build the next wave of intelligent agents
• You will participate in the end-to-end AI product development lifecycle, designing and developing scalable deep learning and generative AI systems and services
• You will collaborate with AI Platform Engineers and Software Engineers to define requirements and develop reusable workflows and ML pipelines
• Design and deliver scalable RAG services that can be integrated with numerous applications, support thousands of tenants, and operate at scale in production
• Drive system efficiencies through automation, including capacity planning, configuration management, performance tuning, monitoring, and root cause analysis
• Participate in periodic on-call rotations and be available to resolve critical issues
• Collaborate with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to understand customer requirements, design prototypes, and bring innovative technologies to production
• Engage in fun-spirited yet thought-provoking conversations with your team around fun topics, such as: Should the popularity of penguins influence conservation efforts? Is creativity more like a neural network or a heuristic algorithm? How would society evolve if humans communicated through colors as well as words?