Software Engineering MTS/SMTS- AI Platform Services

tion and the goal of Salesforce is to combine AI, Data and CRM to build the
most customer oriented CRM solution. AI/ML is the key pillar of it by integrating and weaving
multiple AI based solutions into Salesforce offerings. With so much happening in the AI world
including https://Generative.AI revolution, Salesforce AI Platform is the core of this revolution by
building common ML Platform services for all AI/ML projects.

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.
We are looking for Engineering leaders 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 don't expect you to be the most technical person on your team, but there
is a pretty high minimum bar that you must pass to be useful to the team, and help
influence the team to make the right technical decisions.
● 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 Big Data services 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
Core Qualifications:
● BS, MS, or PhD in computer science or a related field, or equivalent work experience
with 3 to 10 years of experience
● Hands-on experience with big data, machine learning, and microservices
architectures
● Track record of leading highly impactful projects from conception to production
● Expertise in JVM based languages (Java, Scala) and 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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