Job Description

Description

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.

What you’ll do:

• Design and deliver scalable generative AI services that can be integrated with many applications, thousands of tenants, and run 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 for critical issues.

• Partner with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to understand customer requirements, design prototypes, and bring innovative technologies to production

• Participate in meal conversations with your team members about really important topics, such as: Should the cuteness of panda bears be a factor in their survivability? Is love a decision tree or a regression model? How far ahead would society be today if we had 12 fingers instead of 10?

Required Skills:

• 4+ years of industry experience of ML engineering in building AI system and/or services.

• 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

• Strong experience building and applying machine learning models for business applications

• Proven ability to implement, operate, and deliver results via innovation at large scale

• Experience with distributed, scalable systems and modern data storage, messaging and processing frameworks, including Kafka, Spark, Docker, Hadoop, etc.

• Grit, drive and a strong feeling of ownership coupled with collaboration and leadership.

Preferred Skills:

• Experience in developing deep learning models with complex business use cases and big amount of unstructured data.

• Solid Machine Learning Engineering background and familiarity with state-of-the-art deep learning techniques especially for NLP.

• Expertise with applying LLMs, prompt design, and fine-tuning methods

• Strong background in ML approaches and techniques, ranging from Artificial Neural Networks to Bayesian methods

• Experience with conversational AI

• Fantastic problem solver; ability to solve problems that the world has not solved before

• Excellent written and spoken communication skills

• Demonstrated track record of cultivating strong working relationships and driving collaboration across multiple technical and business teams

For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.

Job Tags

Full time,

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