Software Engineering PMTS

Salesforce•Seattle, WA

1d•$197,300 - $313,700

About The Position

As a Principal Engineer (PMTS), you will play a key role in defining and driving the architecture, design, and evolution of large-scale distributed systems and platform infrastructure. You will partner with engineering, product, and operations teams to build highly scalable, resilient, and secure systems that power critical business capabilities. This role requires deep technical expertise, strong architectural vision, and the ability to influence engineering strategy across multiple teams and organizations. At Salesforce, you'll work at the intersection of enterprise AI and platform engineering — using and building AI-powered tools to accelerate development, automate operations, and shape the future of how software is built and deployed at scale.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
10+ years of experience designing, building, and operating large-scale software systems and distributed platforms.
Demonstrated success leading architecture and technical strategy across multiple teams and complex initiatives.
Experience building highly available, mission-critical systems operating at scale.
Experience working with or evaluating AI/ML systems, AI-powered developer tooling, or agentic workflows is strongly preferred.
Deep expertise in distributed systems architecture, cloud-native technologies, and modern software engineering practices.
Strong experience with public cloud platforms such as AWS, Azure, or Google Cloud.
Proficiency with containerization and orchestration technologies such as Docker and Kubernetes.
Strong understanding of networking, security, service discovery, load balancing, DNS, and data management systems.
Experience with microservices architectures, API design, event-driven systems, and asynchronous communication patterns.
Familiarity with observability, monitoring, logging, and reliability engineering practices.
Experience with CI/CD pipelines, infrastructure automation, and DevOps methodologies.
Strong understanding of performance engineering, scalability patterns, and distributed data systems.
Familiarity with AI and ML concepts — including LLMs, model deployment, inference infrastructure, and AI agent frameworks — as they apply to platform and infrastructure engineering.
Direct, applied experience with AI-powered tools such as GitHub Copilot, Agentforce, Einstein, or equivalent AI coding and operations platforms.
Exceptional communication, collaboration, and stakeholder management skills.
Ability to influence technical direction across organizational boundaries.
Strong analytical and problem-solving skills with a focus on pragmatic execution.
Proven ability to mentor engineers and drive technical excellence.

Nice To Haves

Experience operating systems at internet scale or supporting high-volume enterprise workloads.
Expertise in platform engineering, developer experience, or infrastructure architecture.
Relevant certifications in cloud architecture, distributed systems, security, or DevOps are a plus.
Experience building or operating AI/ML infrastructure, LLM serving layers, or agentic systems at scale.
Familiarity with responsible AI principles, AI governance frameworks, and AI risk management.

Responsibilities

Define and document high-level and detailed architectures for large-scale distributed systems, platform services, and infrastructure components.
Design solutions that prioritize scalability, reliability, performance, security, and operational excellence.
Establish architectural standards, design patterns, and engineering best practices across the organization.
Evaluate and integrate AI-assisted design and documentation tools to accelerate architecture review cycles and improve decision quality.
Provide technical leadership and mentorship to engineering teams throughout the software development lifecycle.
Drive technical decision-making, architecture reviews, and technology evaluations.
Influence long-term platform and system strategy through thought leadership and hands-on engagement.
Champion the adoption of AI-powered developer tools, including AI coding assistants, automated code review, and intelligent CI/CD systems.
Partner with product managers, engineering leaders, operations teams, and other stakeholders to understand business requirements and translate them into robust technical solutions.
Facilitate alignment across multiple teams working on interconnected platforms and services.
Design and evolve cloud-native platforms, microservices architectures, and distributed applications capable of operating at large scale.
Lead efforts related to service reliability, fault tolerance, observability, performance engineering, and operational efficiency.
Drive adoption of modern platform technologies, automation, and developer productivity tools — including AI-driven automation, intelligent monitoring, and agentic operations tooling.
Stay current with emerging technologies, industry trends, and architectural approaches — including large language models (LLMs), AI agents, and AI-augmented engineering workflows.
Evaluate and introduce new technologies, frameworks, and patterns where they provide measurable business or technical value.
Contribute to the organization's long-term technology roadmap.
Identify opportunities to apply AI capabilities — such as Agentforce, Salesforce Einstein, and third-party AI platforms — to drive platform efficiency and innovation.
Identify architectural, operational, and scalability risks early and develop mitigation strategies.
Ensure systems meet availability, resilience, disaster recovery, security, and compliance requirements — including responsible AI governance and AI system risk controls.
Champion operational excellence and engineering rigor across teams.
Analyze system performance characteristics and drive optimization initiatives.
Design systems capable of handling significant growth in scale, traffic, data volume, and complexity.
Establish performance benchmarks and capacity planning strategies.
Maintain comprehensive architectural documentation and design artifacts.
Mentor engineers and architects through technical reviews, design sessions, and knowledge-sharing initiatives.
Promote engineering excellence through documentation, standards, and best practices.
Use AI-powered knowledge management and documentation tools to accelerate artifact creation and keep architecture documentation current.