Software Engineer, Salesforce

• 5+ years of experience building and maintaining large-scale distributed systems, data-intensive applications, or cloud-native services
• Proficiency in system design, microservices architecture, and cloud-native tools (Docker, Kubernetes, etc.)
• Strong programming skills in Python and/or Java, with a deep understanding of system-level performance
• Experience building scalable data pipelines using Kafka, Spark, Flink, or similar frameworks
• Deep understanding of modern data storage, indexing, and retrieval frameworks (e.g., Elasticsearch, Redis, Hadoop, Cassandra)
• Familiarity with LLMs, embeddings, and retrieval-based architectures (e.g., RAG, vector search)
• Proven ability to take projects from ideation to production, with a strong bias for execution
• Solid understanding of API design, versioning, and deployment in high-availability environments

Desirable

• Salesforce is leading the next wave of enterprise AI by building intelligent agents that deliver customer success 24/7
• We're investing in cutting-edge Retrieval-Augmented Generation (RAG) systems that power generative AI platforms and applications used by millions of people globally
• We’re looking for a Software Engineer to join the Einstein Foundations team, focused on building scalable, reliable, and intelligent distributed systems that support RAG pipelines and services across Salesforce
• As a Software Engineer on the Einstein RAG team, you'll play a key role in designing, implementing, and maintaining large-scale distributed systems that integrate deep learning models, retrieval pipelines, and enterprise data
• You'll work across the AI stack—from building microservices and data pipelines to enabling real-time inference and document retrieval using modern ML techniques
• You’ll partner with ML engineers, product managers, and researchers to operationalize LLMs and RAG services for production use at scale
• Design and build robust, scalable RAG systems that serve thousands of tenants and integrate with multiple Salesforce applications
• Develop high-performance distributed systems for knowledge retrieval, document ranking, and grounding LLMs in structured and unstructured enterprise data
• Build scalable APIs, microservices, and orchestration layers to support multi-stage RAG pipelines
• Drive automation for deployment, monitoring, performance tuning, and root cause analysis
• Collaborate with ML and AI platform teams to productionize retrieval services and integrate vector databases, embeddings, and search infrastructure
• Ensure reliability, security, and performance of AI-backed systems in real-time, multi-tenant environments
• Participate in on-call rotations and be a key player in debugging live issues and ensuring high availability