Key skills
Machine learningNatural language processingTransformer architecturesPrompt engineeringRetrieval augmented generation (RAG) systemsVector-based information retrievalContext engineeringDistributed systems architectureREST microservicesMCP microservicesObservability and monitoringPyTorchTensorFlowPythonOpen weight large language models (LLMs)Embedding modelsVector search technologiesFAISSPineconeCI/CD pipelinesModel monitoring frameworksStatistical validationNamed entity recognition (NER)Entity resolutionMLOpsLLMOpsClaude CodeCodexCursorExecutive communicationGoAIMachine LearningMLNLPClaudeRAGAgenticCI/CDProduct ManagementCommunicationCollaboration
About this role
ZoomInfo is a Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights and advanced automation. They are seeking a Senior Machine Learning Engineer to build scalable recommendation systems, optimize NLP and embedding systems, and manage MLOps infrastructure to enhance AI system performance.
Responsibilities:
- Build large scale recommendation systems utilizing embeddings generated for structured and unstructured data using methods such as a two tower architecture
- Performant recommendation designs which can scale to millions of recommendations per day for different product features
- Utilize graph based structures, search and scoring to enhance recommendation quality
- Fine-tune (LORA/PEFT), customize and deploy embedding models (LLMs/SLMs) for multi-language text understanding and semantic search
- Architect vector search solutions that enable language-agnostic clustering and classification across global datasets
- Build and optimize high-performance retrieval systems using vector databases
- Architect and manage scalable MLOps and LLMOps infrastructure for robust model training, evaluation, deployment, and monitoring systems
- Design comprehensive CI/CD pipelines, implement model monitoring frameworks to identify drift patterns, and ensure high availability and fault tolerance
- Help establish metrics, experimentation frameworks, and statistical validation approaches for AI system performance
- Design and implement agentic systems for automated web extraction, NER, and entity resolution tasks
- Build comprehensive evaluation frameworks for agent performance across data acquisition and processing workflows
- Create feedback loops that continuously improve agent decision-making and data quality outcomes
- Build, and scale MCP servers and integrate them into broader AI and product ecosystems
- End to end ownership of production workflows with close collaboration across engineering teams managing data, application, API and MCP layers to ensure models integrate seamlessly and scale with business needs
- Work with Product Management to translate business requirements into scalable ML solutions
Requirements:
- 6+ years hands-on ML/NLP experience (or 3+ years post-PhD/Master's) with at least two delivered, revenue-impacting products in production environments
- Expertise in modern AI architectures including transformer stacks, prompt engineering, RAG systems, vector-based information retrieval and context engineering
- Proven track record building and managing production systems by architecting and deploying scalable distributed systems of REST & MCP based microservices for applications and agents with observability and monitoring of latency, token utilization and system reliability
- Strong applied research capabilities (PyTorch or TensorFlow) paired with software-engineering rigor (Python) and familiarity with open weight LLMs (QWEN, Gemma, OSS) and embedding models and vector search technologies (FAISS, Pinecone)
- Executive communication skills with ability to persuade technical and non-technical audiences through data-driven storytelling, comfortable owning strategy, budget, and cross-functional collaboration
- Utilize modern AI development tools (Claude Code, Codex, Cursor) in their engineering workflow to maximize development velocity and code quality