CareerPlanGet AI match score →

Software Engineering SMTS - AI Cloud

2 Locations💼 Full-time🗓 2026-07-09 → 2026-07-30

Core

Design and deliver scalable generative AI services integrated with applications and tenants, driving system efficiencies through automation and performance tuning.

Role type

Senior IC Machine Learning Engineer (Generative AI Infrastructure)

Builds

Scalable generative AI services, microservices, and ML APIs for Salesforce Einstein/Agentforce

Domain

Cloud Infrastructure, Generative AI, Distributed Systems

Deliverable

production ML models

Required skills

ML engineering, distributed microservices, containerized deployment (Kubernetes), large-scale system operations, data storage/messaging frameworks

Preferred skills

MLOps/ML Infra workflows, building ML models for business applications, large-scale distributed ML technologies

Technologies

AWS, GCP, Kubernetes, Spinnaker, Kafka, Spark, Docker, Hadoop, Sagemaker, Tensorflow, Pytorch, Triton

Responsibilities

Design and deliver scalable generative AI services; drive system efficiencies through automation and performance tuning; participate in on-call rotations for critical issues; partner with Product Managers and Data Scientists to bring innovative technologies to production

Seniority

Senior, hands-on IC

Rewrite
## Responsibilities - 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. ## Requirements - 4+ years of industry experience of ML engineering in building AI systems and/or services. - Experience designing and building distributed microservices on AWS, GCP or other public cloud substrates. - Experience using modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies. - 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. ## Nice to Have - 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. - Understanding of MLOps/ML Infra workflows, processes and ML components. - Strong experience building and applying machine learning models for business applications. - Working or academic knowledge with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies. ## Benefits When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
Sourced via workday · Listed on CareerPlan, which tracks 70,000+ jobs from 20+ sources.
Apply on Workday ↗