Software Engineering Architect

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Job Category
Software Engineering
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
Digital Enterprise Technology (DET) at Salesforce builds the internal engines that power our business. The Machine Learning & Innovation (MLI) team is the applied science and infrastructure strike-force responsible for "Foundry," our unified ML/AI Platform. We operate as "Customer Zero," building the high-velocity, real-time streaming feature platforms and recommendation models that power internal products like the Events App and enterprise-wide personalization.
Role Overview:
As a Principal ML Engineer, you will serve as the lead infrastructure and inference runtime engineer for the Foundry platform. You will be responsible for bridging complex mathematical model code with resilient cloud compute infrastructure. You will own the automation, orchestration, and containerization of our machine learning training and serving environments, ensuring that our data scientists can iterate rapidly while maintaining enterprise-grade production stability.
What You'll Do:
Own the inference runtime and environment automation across the Foundry/AWS ecosystem.
Architect and provision Metaflow and SageMaker Studio infrastructure-as-code modules for multi-tenant developer environments.
Containerize complex ML deployment workflows, ensuring models can transition seamlessly from experimental notebooks to high-throughput production endpoints.
Design and implement scalable CI/CD pipelines specific to machine learning artifacts, managing model versioning, testing, and promotion.
Troubleshoot and optimize deeply technical bottlenecks between cloud network topology (VPC, IAM) and ML framework execution.
Define and enforce best practices for ML infrastructure reliability, security, and developer velocity across the engineering organization.
What You Bring:
15+ years of full-stack software and infrastructure engineering experience, with a deep specialization in ML engineering and MLOps.
Expertise in cloud infrastructure automation (AWS, Terraform) and container orchestration (Docker, EKS).
Strong proficiency in Python and familiarity with machine learning workflow orchestrators (Metaflow, Airflow, Kubeflow).
Proven experience deploying and managing models in production environments (SageMaker, FastAPI, TorchServe).
Deep understanding of cloud security perimeters, IAM role assumption, and secure data access patterns in distributed ML platforms.
A strong "builder" mindset with the ability to take ambiguous infrastructure requirements and deliver reliable, code-driven solutions.
Unleash Your Potential
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.
Accommodations
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Posting Statement
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