Staff Machine Learning Engineer

Location: San Mateo, CA (hybrid 2 days onsite a week)AI System DevelopmentSearch & Recommendation SystemsLead the design and implementation of advanced Search, Ranking, and Recommendation systems to help customers navigate millions of technical products.Doc Extraction & NLPDevelop high-precision NLP and document extraction pipelines to digitize and structure complex construction data from unstructured sources.Advanced ArchitectureResearch and implement novel deep learning architectures, focusing on hybrid retrieval models and fine-tuned LLMs.Production DeploymentDevelop, train, and deploy deep learning and machine learning models that are scalable, extensible, and integrated into production environments.Agentic WorkflowsArchitect autonomous or semi-autonomous agents that can plan and execute multi-step discovery tasks.Full Product Lifecycle ParticipationTechnical LeadershipCollaborate with product managers and UX designers to integrate AI components into fully functional systems, providing technical guidance on feasibility and architecture.End-to-End OwnershipParticipate in the complete product lifecycle—from concept design to development, integration, testing, and deployment.Scalable SolutionsHigh-Volume DataBuild products that handle large data volumes efficiently while remaining highly scalable for onboarding new clients.Pipeline DesignDesign complete end-to-end data and ML pipelines, ensuring seamless integration and monitoring in production.Research & CollaborationR&D InitiativesWork closely with the leadership team on research efforts to explore cutting-edge technologies, such as vector databases and embedding-based retrieval.Excellence StandardsUphold a culture of engineering excellence by maintaining high standards in code quality, documentation, and innovationMinimum QualificationsEducationBachelor's or Master's degree (PhD preferred) in Science or Engineering with strong programming and analytical skills.ML ExpertiseStrong conceptual understanding of machine learning principles, specifically in NLP, Search, or Ranking.Technical SkillsHands-on experience implementing ML projects in Python using libraries like NumPy, scikit-learn, and pandas.Deep LearningProficiency in training and fine-tuning deep learning models using PyTorch or TensorFlow.LeadershipProven ability to lead technical initiatives from concept to operation while navigating complex challenges.Preferred QualificationsSpecialized InfrastructureDeep experience with Vector Databases (e.g., Pinecone, Milvus) and optimizing embedding models for retrieval.Fine-tuningExperience fine-tuning LLMs for specialized domain tasks and ranking signals.AI Agent OrchestrationHands-on experience with agentic frameworks (e.g., LangGraph, AutoGen, or CrewAI) for building complex, multi-step reasoning chains.Planning & MemoryExperience implementing agentic "memory" (long-term/short-term) and planning strategies (like ReAct or Tree of Thoughts).Data StructuresExpert knowledge of algorithms and data structures.Research & Community: A track record of publications in top-tier conferences (e.g., NeurIPS, SIGIR, KDD, ACL) or significant contributions to open-source ML projects.