Machine Learning Engineer at Salesforce AI Research working closely with teams to design agentic AI systems. Innovating at the frontier of AI with transformative solutions for Salesforce customers.
About the role
- Senior Applied AI Scientist at Messagepoint developing generative AI solutions using large language models. Focused on transforming research into production systems for customer communications.
Responsibilities
- Design and deploy production multi-agent AI systems using frameworks like LangGraph, OpenAI Assistants/Responses, Swarm, CrewAI, or AutoGen to solve complex business problems at scale
- Build production RAG pipelines implementing proven retrieval techniques including hybrid search, reranking, and knowledge graph integration for enterprise-grade performance
- Engineer and optimize prompts as versioned code using established techniques (Chain-of-Thought, ReAct, self-consistency) with rigorous testing and evaluation
- Implement comprehensive evaluation and monitoring pipelines with production KPIs, quality gates in CI/CD, and automated regression testing
- Architect scalable agentic workflows with persistent state management, error recovery, and production-grade reliability
- Deploy observability and debugging infrastructure using LangSmith, TruLens, DeepEval for production monitoring and issue resolution
- Integrate safety and compliance measures including PII detection, content filtering, guardrails, and security best practices
- Optimize system performance and costs through caching strategies, model routing, batch processing, and efficient resource utilization
- Build APIs and microservices for AI systems with proper versioning, documentation, and SLA management
- Champion AI-assisted development using tools like Claude Code, Augment, and Gemini CLI to maximize engineering productivity
- Stay current with relevant AI research - Read papers from top conferences (NeurIPS, ICML, ACL) to identify techniques applicable to our use cases
- Evaluate emerging techniques - Assess new methods for potential adoption, focusing on production viability and ROI
- Implement proven research concepts - Translate well-validated research into production when it provides clear value
- Conduct targeted experiments - Test specific hypotheses about system improvements with data-driven validation
Requirements
- 5+ years of software engineering experience with at least 2 years shipping LLM-powered products to production
- MS in Computer Science or equivalent experience (PhD is a plus but not required) with strong foundation in deep learning, NLP, or machine learning
- Solid understanding of transformer architectures, attention mechanisms, and modern deep learning techniques
- Ability to read and understand research papers to stay current with the field and identify useful techniques
- Proven track record shipping LLM applications with real users, SLAs, and production monitoring
- Production deployment of multi-agent systems using frameworks like LangGraph, OpenAI Assistants, or similar
- Experience building RAG pipelines at scale with vector databases, retrieval optimization, and quality metrics
- Expert-level prompt engineering with systematic testing and optimization approaches
- Building APIs and microservices for AI systems with proper error handling and scalability
- Distributed computing and storage experience (e.g., Apache Spark, Ray, cloud storage solutions)
- Strong software engineering practices including code review, testing, CI/CD, and documentation
- Active use of AI-assisted development tools like Claude Code, Augment, Gemini CLI to maximize productivity
- Experience with production ML/AI infrastructure including monitoring, debugging, and performance optimization
- Python expertise with async programming and modern web frameworks (FastAPI, Django)
- Vector database experience (pgvector, Pinecone, Weaviate, Milvus, or FAISS)
- LLMOps/AgentOps tools familiarity (LangSmith, TruLens, DeepEval, Giskard)
- SQL and search systems experience (PostgreSQL, MySQL, Elasticsearch, Solr, OpenSearch)
- Cloud platform proficiency (AWS, GCP, or Azure) with containerization and orchestration
- API design and microservices architecture for AI systems
- Version control and collaborative development practices with Git
Benefits
- Access to latest models, robust compute infrastructure, and top-tier development tools
- Support for attending major AI conferences to stay current with the field
- Focus on sustainable engineering practices and healthy team dynamics
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