Architect, Applied Science - AgentForce

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Software Engineering

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.

The Agent Force Data Science team powers the core Large Language Models (LLMs) and reasoning engines behind Salesforce's production-grade AI agents. Our work sits at the critical junction of generative AI research and massive-scale engineering, enabling trustworthy, high-performance AI systems across sales, service, marketing, and analytics.

We are looking for a visionary technical leader to architect the next generation of our AI platform-bridging the gap between cutting-edge model development and robust, scalable production infrastructure.

• System Architecture & Technical Strategy:
  Define the end-to-end architecture for Agent Force'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 with production traffic while preserving latency and stability.
• Design systems for multi-turn agent state management, memory persistence, and tool invocation (function calling).
• Product & Application Architecture:
  Own end-to-end architectural design of Agent Force AI capabilities from product requirements through model design, system implementation, and production rollout.
• Translate product use cases into concrete system architectures, including APIs, service contracts, and model interaction patterns.
• Define reference architectures for AI-powered applications that standardize product integration with Agent Force models.
• Partner with Product Engineering to ensure AI capabilities are designed for usability, reliability, and developer experience, not just model quality.
• Applied Science Leadership:
  Translate abstract research concepts into concrete engineering specifications and lead evaluation frameworks to measure real-world system performance (latency, cost-per-token, reliability).
• Collaborate with scientists to optimize models for deployment (quantization, distillation, pruning) without sacrificing reasoning capabilities.
• Cross-Functional Collaboration:
  
  Serve as the primary architectural liaison between Applied Science, Product Engineering, Infrastructure/AI Engineering, and Product Management.
• Establish best practices for MLOps, model versioning, and safe rollout strategies (canary deployments, shadow testing) specific to GenAI.
• Mentor Principal Scientists and Senior Engineers on system design principles and architectural patterns.

• Education & Experience:
  
  PhD or Master's in Computer Science, AI, Machine Learning, or Distributed Systems; 10+ years of technical experience deploying ML models at scale; proven experience as an Architect or principal-level technical lead for large-scale AI or data platforms.
• Technical Expertise:
  Experience designing and building production-grade AI-powered applications or platforms; experience defining public/internal APIs, SDKs, and service interfaces for ML/AI capabilities.
• Frontend-Backend-Model interaction patterns for low-latency AI experiences.
• Deep Learning & LLMs:
  Transformer architectures, attention mechanisms, and the math behind LLMs.
• Inference & Optimization:
  High-performance inference serving and optimization techniques (quantization, LoRA adapters, paged attention).
• Distributed Systems:
  Designing distributed systems, microservices, and event-driven architectures (Kafka, gRPC, Kubernetes).
• Coding:
  Proficiency in Python; familiarity with C++ or CUDA is a plus.
• Architectural
  
  Competencies:
  
  Designing for constraints, agentic workflows, and vector stores/search infrastructure for RAG (e.g., FAISS, Weaviate, Elasticsearch).

• Experience architecting platforms for Reinforcement Learning (RL) in production.
• Ability to map product requirements to system architecture, model design, and infrastructure choices.
• Strong intuition for user experience constraints (latency, streaming, partial results, fallbacks).
• Experience balancing feature velocity with platform stability.
• Active contributor to open-source LLM infrastructure projects (e.g., Ray, Lang Chain, Hugging Face).
• Experience with safety guardrails and…