Salesforce.com, Inc Lead/Principal ML Engineer in Bellevue, Washington
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Products and Technology
Salesforce is looking for a Lead/Principal Data ML Engineer with Java and/or Python experience, to help us take on one of the world’s most extensive data sets and transform it into amazing products that feel like magic. You will work on cutting-edge AI applications and products used by millions of people every day.
Einstein.ai (https://gus.lightning.force.com/lightning/r/0F9B00000002yhlKAA/view) Platform (under Salesforce Research) democratizes AI and transforms the way Salesforce builds and delivers trusted machine learning products, solutions and services. The Einstein.AI Platform augments the Salesforce Platform with the ability to easily create, deploy, and utilize predictive applications on unstructured data across all clouds. We achieve this by providing a set of end-to-end solutions to use-cases in Natural Language Processing and Computer Vision powered by Deep Learning (DL) and exposed via easy-to-use public APIs. The platform currently supports different types of models such as Vision (Image Detection & Classification, OCR, PDF parser etc) and Text (Intent, NER, Sentiment etc). Chatbots, Industries, Marketing Cloud are a few important consumers of our APIs to power their deep learning capabilities.
We have a real fun team that is extremely smart and motivated and they are looking for equally motivated Platform engineers with experience building large-scale deep learning training and real-time serving pipelines, to help us get to the next level, and build a platform that scales to thousands of customers, and billions of predictions per day.
We're looking for strong candidates with a beginner's mindset ready to innovate, iterate, and deliver GA quality services!
Own the end to end architecture of the Einstein.AI Platform and build the technology for scaling the Einstein.AI Platform to thousands of Salesforce customers, and millions of predictions per day.
Partner with Product Managers and Data Scientists to understand customer requirements and design prototypes and bring ideas to production
Develop compute and data infrastructure capable of ingesting and processing customer data from different sources at scale in batch and real-time
Build DL model A/B testing and promotion flow in a high-volume production environment
Build multi-tenant RESTful services supporting the full lifecycle of training models on customer data to low-latency prediction/inferences at scale.
Help with the larger initiative of building a unified ML platform for Salesforce.
What we care about?
Professional industry experience is preferred; especially with Java and Python. We develop real products. You need to be an expert in coding.
We have scale. You need to have experience in distributed, scalable systems. Consistency / availability tradeoffs are made here. You’ve tinkered with modern data storage, messaging, and processing frameworks (Kafka, Spark , Hadoop, Cassandra, etc.). You know how to put modern data / ML pipelines together using these frameworks at your disposal.
You should have some Machine Learning or Deep Learning experience. NLP expertise is a huge plus.
We are a growing, diverse team and we work together on projects. We love to collaborate and help each other, and we want someone to share that ideology.
You have to be a very quick learner - we face new challenges every day, anything that ranges between the operating model of a financial services companies, conversation model for chatbots, tinkering with convolutional and recurrent networks, to how to make Spark work with the S3 file system. No school could prepare you for all of these, so you need to be very quick on your feet.
You should be a self-starter who can see the big picture, and prioritize your work to make the largest impact on the business’ and customer’s vision and requirements.
We are looking for experienced candidates (5+ years of full time software engineering experience)
2+ years in data / ML / DL settings
We run on AWS. We dockerize applications. You should know how to build, test, and deploy code to run on cloud infrastructure.
Experience with Deep Learning for NLP.
Experience with Tensorflow and PyTorch.
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