Salesforce · 10 hours ago
Architect, Applied Science – AgentForce
Palo Alto, CA
Full-time
Hybrid
Lead/Staff
$218K/yr - $365K/yr
10+ years expSalesforce is the #1 AI CRM, where humans with agents drive customer success together. The Architect, Applied Science will define the technical vision and system design for AgentForce’s AI capabilities, focusing on architecting scalable pipelines, optimizing inference, and collaborating across teams to ensure cohesive solutions.
Agentic AIArtificial Intelligence (AI)Cloud ComputingCRMSaaSSales EnablementSoftware
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
Partner with Product Engineering to ensure AI capabilities are designed for usability, reliability, and developer experience, not just model quality
Translate abstract research concepts into concrete engineering specifications. (e.g., "How do we architect a system that supports Speculative Decoding or KV-Caching at our specific scale?")
Lead the design of evaluation frameworks that move beyond academic benchmarks 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
Serve as the primary architectural liaison between Applied Science, Product Engineering, Infrastructure/AI Engineering, and Product Management, ensuring cohesive end-to-end solutions
Act as a technical partner to product teams to shape roadmaps, feature designs, and architectural trade-offs involving AI capabilities
Establish best practices for MLOps, model versioning, and safe rollout strategies (canary deployments, shadow testing) specific to GenAI
Mentor Principal Scientists and Staff Engineers on system design principles and architectural patterns
Qualification
Required
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
Deep Learning & LLMs: Profound understanding of Transformer architectures, attention mechanisms, and the math behind LLMs (not just API usage)
Inference & Optimization: Experience with high-performance inference serving (e.g., vLLM, TensorRT-LLM, TGI, Triton) and optimization techniques (quantization, LoRA adapters, paged attention)
Distributed Systems: 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
Ability to design for constraints: balancing model performance (accuracy) against system constraints (latency, throughput, COGS/compute costs)
Experience designing architectures for 'Agentic' workflows (planning, reasoning, tool use, memory)
Familiarity with vector stores and search infrastructure (e.g., FAISS, Weaviate, Elasticsearch) for RAG implementations
Preferred
Experience architecting platforms for Reinforcement Learning (RL) in production environments
Ability to map product requirements → system architecture → model design → infrastructure choices
Strong intuition for user experience constraints (latency, streaming, partial results, fallbacks)
Experience balancing feature velocity vs. platform stability
Active contributor to open-source LLM infrastructure projects (e.g., Ray, LangChain, Hugging Face)
Experience with safety guardrails and governance architectures for Enterprise AI
Benefits
Time off programs
Medical
Dental
Vision
Mental health support
Paid parental leave
Life and disability insurance
401(k)
Employee stock purchasing program
Company
Salesforce
Salesforce is a cloud-based software company that provides customer relationship management software and applications.
10001+ employees
H1B Sponsorship
Salesforce has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (1883)
2024 (2296)
2023 (1850)
2022 (2849)
2021 (2124)
2020 (1960)
Funding
Current Stage
Public CompanyTotal Funding
$65.38MKey Investors
Starboard ValueEmergence CapitalHalsey Minor
2022-10-18Post Ipo Equity
2004-06-23IPO
2003-01-01Series Unknown· $1M
Leadership Team
Arundhati Bhattacharya
Chairman & Ceo Salesforce India
Kendall Collins
CEO, GovCloud - Salesforce
Recent News
2026-02-06
2026-02-06
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