Senior Manager, Performance Engineering – AI Platform (Agentforce & AI Cloud)

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. 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. Role Description The AI Platform Performance, Scale & Cost-to-Serve (CTS) team is seeking a Senior Manager, Performance Engineering to lead performance, scalability, and cost optimization for Agentforce AI and AI Cloud platforms . In this role, you will define and execute the end-to-end performance strategy across agentic AI systems, LLM workflows, and multi-model ecosystems , ensuring enterprise-grade latency, scalability, reliability, and cost efficiency . You will lead a high-impact team focused on benchmarking, optimization, CTS/TCO modeling, and capacity engineering , supporting AI systems that power millions of transactions globally . Join a highly technical and collaborative team at the forefront of Agentic AI innovation . As part of Salesforce’s GenAI initiatives, this team is responsible for scaling and optimizing LLM-driven applications, trust models (toxicity, PII), and agent workflows across a global footprint. The team partners closely with Engineering, Infrastructure, Product, and Data Science to deliver performance excellence at scale , enabling customers to maximize the value of AI across the Salesforce ecosystem. Key Responsibilities People Leadership & Execution Lead, mentor, and grow a team of performance engineers and SREs Define team vision, roadmap, and execution aligned with AI Platform strategy Drive hiring, onboarding, and retention of top engineering talent Establish best practices, frameworks, and standards across performance engineering Manage prioritization, resource allocation, and delivery across multiple programs Foster a culture of ownership, innovation, and operational excellence Performance & Scalability Engineering Own performance strategy for Agentforce AI systems, ensuring scalability for millions of transactions with low latency and high availability Define and track key SLIs/SLOs (TTFT, P95/P99 latency, throughput, error rates) Optimize end-to-end agent workflows (planner → LLM → tools → actions) Drive architectural improvements across application, infrastructure, and model layers Benchmarking & Optimization Lead benchmarking of GenAI models and agentic workflows across multiple providers Identify bottlenecks across GPU utilization, inference pipelines, APIs, and storage systems Leverage production telemetry and controlled experiments to validate performance improvements Continuously improve system efficiency balancing performance, cost, and quality Automation, Tooling & Observability Build scalable performance automation frameworks integrated with CI/CD pipelines Develop tooling for benchmarking, regression detection, and performance analytics Implement observability across distributed systems using tools such as k6, Locust, JMeter, Splunk, Grafana, and APM platforms Enable continuous performance validation in pre-prod and production environments Knowledge of Unit of Scale/ Cost-to-Serve (CTS) Cross-Functional Leadership Partner with Product, Engineering, Infrastructure, Data Science, and FinOps teams Influence architecture decisions to improve scalability, reliability, and cost efficiency Translate business goals into actionable performance ... (truncated, view full listing at source)