Lead ML Engineer

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About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

About Salesforce
We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

Einstein products & platform democratizes AI and transforms the way our Salesforce Ohana builds trusted machine learning and AI products - in days instead of months. It augments the Salesforce Platform with the ability to easily create, deploy, and manage Generative AI and
Predictive AI applications across all clouds. We achieve this vision by providing unified, configuration-driven, and fully orchestrated machine learning APIs, customer-facing declarative interfaces and various microservices for the entire machine learning lifecycle including Data, Training, Predictions/scoring, Orchestration, Model Management, Model Storage, Experimentation etc.

We are already producing over a billion predictions per day, Training 1000s of models per day along with 10s of different Large Language models, serving thousands of customers. We are enabling customers' usage of leading large language models (LLMs), both internally and externally developed, so they can leverage it in their Salesforce use cases. Along with the power of Data Cloud, this platform provides customers an unparalleled advantage for quickly integrating AI in their applications and processes.

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We are looking for passionate Machine Learning Engineers and Backend Engineers to help us take us to the next level, and build a platform that scales to hundreds of thousands of customers, and hundreds of billions of predictions per day and works on bleeding edge technologies on model training, model inferencing and Generative AI.

The ideal candidate will be:

● Technical - We are looking for passionate and code geeks developers who analyze business problems and evolve technical solutions in the most optimal and simple ways. Sometimes engineers wear multiple hats to drive their projects end-to-end, thinking holistically and compare from available set of technologies to drive best decisions technically
● A Leader - You are a natural leader, who can mentor and coach engineers on the team to be able to handle bigger challenges, find fulfillment in their work, and execute on the product growth goals through collaboration to do the best work of their lives.

● Experienced - We will need you to bring that experience. We want the best people who spend large portions of their time thinking about how to design large scale distributed Machine Learning services.
● Team Player - You will drive collaboration, efficiency and communication by liaising with your peers, leadership, product and program management and cross teams. You will support / seek timely help with your peers, communicate risks and mitigation plans with leadership and communicate closely with product managers to iteratively build AI Platform services which caters to our users and business use cases

Responsibilities:

● Working with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies on a modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies
● Experience building Distributed microservices on AWS, GCP or other public cloud substrates
● Eat, sleep, and breathe services. You have experience balancing live-site management, feature delivery, and retirement of technical debt
● Partner with Product Managers, Architects and Data Scientists to understand customer requirements, and help translate requirements to working software
● Own the technology for fully orchestrated machine learning APIs for Einstein Platform
● Contribute to the long-range plan, and help drive the microservices architectures for machine learning
● Designing, developing, debugging, and operating resilient distributed systems that run across thousands of compute nodes in multiple datacenters
● Participate in the team’s on- call rotation to address complex problems in real-time and keep services operational and highly available
● Create and enforce processes that ensure quality of work, and drive engineering
excellence
● Exhibit a customer-first mentality while making decisions, and be responsible and accountable for the output of the team
● Partner with vendors like AWS and Data Science teams to pick best fit in terms of libraries and compute to deliver cost effective and scalable model hosting and
tuning/training capabilities
● Work collaboratively in a geographically distributed teams in North America, EMEA and APAC

Core Qualifications:

● BS, MS, or PhD in computer science or a related field, or equivalent work experience with 9 to 15 years of experience
● 3+ years of hands-on experience with designing and developing complex big data, machine learning systems, and microservices architectures
● Track record of leading highly impactful projects from conception to production

● Expertise in JVM based languages (Java, Scala) and/or Python
● Experience leading/working in teams that have built and and run machine learning services, such as for training & inferences, at scale for predictive and generative models
● Experience with open source projects such as Spark, Kafka, Feast, Iceberg
● Experience in building software on AWS cloud computing such as OpenSearch,
DynamoDB, EMR and S3

Preferred Qualifications:

● Experience working in machine learning, and technologies such as Amazon SageMaker, Microsoft Azure ML or Google Cloud ML
● Experience building or leading teams that have built and and run real-time data
applications in production

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