Hybrid Architect, Applied Science – AgentForce

at Salesforce

• Job title: Architect, Applied Science – AgentForce
• Job type: Full Time
• Experience level: Senior, Lead
• Salary: $218,400 - $365,200 per year
• Degree requirement: Postgraduate Degree
• Tech skills: Distributed Systems, GRPC, Kafka, Kubernetes, Microservices, Python
• Location requirements: Hybrid, Palo Alto, United States

About the role

• Architecting AI platform capabilities for Salesforce's AgentForce team. Driving technical vision and system design for deploying large-scale ML models.

Responsibilities

• Define the end-to-end architecture for AgentForce’s model serving, inference orchestration, and agentic reasoning loops.
• Make high-stakes technical decisions regarding "build vs. buy," model sizing, context window management, and retrieval-augmented generation (RAG) strategies.
• Architect scalable pipelines for continuous learning (RLHF/RLAIF) that integrate seamlessly with production traffic without compromising latency or stability.
• Design systems for multi-turn agent state management, memory persistence, and tool invocation (function calling).
• Own the end-to-end architectural design of AgentForce AI capabilities from product requirements through model design, system implementation, and production rollout.
• Translate product use cases (e.g., agent experiences, workflows, UI features) into concrete system architectures, including APIs, service contracts, and model interaction patterns.
• Define reference architectures for AI-powered applications (web, backend services, agent runtimes) that standardize how products integrate with AgentForce models.
• Translate abstract research concepts into concrete engineering specifications.
• Collaborate with scientists to optimize models for deployment (quantization, distillation, pruning) without sacrificing reasoning capabilities.
• Mentor Principal Scientists and Staff Engineers on system design principles and architectural patterns.

Requirements

• PhD or Master’s in Computer Science, AI, Machine Learning, or Distributed Systems
• 10+ years of technical experience, with a specific focus on deploying ML models at scale
• Proven experience acting as an Architect or Principal-level technical lead for large-scale AI or data platforms
• Experience designing and building production-grade AI-powered applications or platforms
• Experience defining public/internal APIs, SDKs, and service interfaces for ML/AI capabilities consumed by product teams
• Familiarity with frontend–backend–model interaction patterns for low-latency user-facing AI experiences
• Profound understanding of Transformer architectures, attention mechanisms, and the math behind LLMs (not just API usage)
• Experience with high-performance inference serving (e.g., vLLM, TensorRT-LLM, TGI, Triton) and optimization techniques (quantization, LoRA adapters, paged attention)
• Strong background in designing distributed systems, microservices, and event-driven architectures (Kafka, gRPC, Kubernetes)
• Advanced proficiency in Python and familiarity with C++ or CUDA is a strong plus.

Benefits

• time off programs
• medical
• dental
• vision
• mental health support
• paid parental leave
• life and disability insurance
• 401(k)
• employee stock purchasing program