Staff Machine Learning Engineer

Location: San Mateo, CA (hybrid 2 days onsite a week)

AI System Development

• Search & Recommendation Systems: Lead the design and implementation of advanced Search, Ranking, and Recommendation systems to help customers navigate millions of technical products.
• Doc Extraction & NLP: Develop high-precision NLP and document extraction pipelines to digitize and structure complex construction data from unstructured sources.
• Advanced Architecture: Research and implement novel deep learning architectures, focusing on hybrid retrieval models and fine-tuned LLMs.
• Production Deployment: Develop, train, and deploy deep learning and machine learning models that are scalable, extensible, and integrated into production environments.
• Agentic Workflows: Architect autonomous or semi-autonomous agents that can plan and execute multi-step discovery tasks.

Full Product Lifecycle Participation

• Technical Leadership: Collaborate with product managers and UX designers to integrate AI components into fully functional systems, providing technical guidance on feasibility and architecture.
• End-to-End Ownership: Participate in the complete product lifecycle—from concept design to development, integration, testing, and deployment.

Scalable Solutions

• High-Volume Data: Build products that handle large data volumes efficiently while remaining highly scalable for onboarding new clients.
• Pipeline Design: Design complete end-to-end data and ML pipelines, ensuring seamless integration and monitoring in production.

Research & Collaboration

• R&D Initiatives: Work closely with the leadership team on research efforts to explore cutting-edge technologies, such as vector databases and embedding-based retrieval.
• Excellence Standards: Uphold a culture of engineering excellence by maintaining high standards in code quality, documentation, and innovation

Minimum Qualifications

• Education: Bachelor’s or Master’s degree (PhD preferred) in Science or Engineering with strong programming and analytical skills.
• ML Expertise: Strong conceptual understanding of machine learning principles, specifically in NLP, Search, or Ranking.
• Technical Skills: Hands-on experience implementing ML projects in Python using libraries like NumPy, scikit-learn, and pandas.
• Deep Learning: Proficiency in training and fine-tuning deep learning models using PyTorch or TensorFlow.
• Leadership: Proven ability to lead technical initiatives from concept to operation while navigating complex challenges.

Preferred Qualifications

• Specialized Infrastructure: Deep experience with Vector Databases (e.g., Pinecone, Milvus) and optimizing embedding models for retrieval.
• Fine-tuning: Experience fine-tuning LLMs for specialized domain tasks and ranking signals.
• AI Agent Orchestration: Hands-on experience with agentic frameworks (e.g., LangGraph, AutoGen, or CrewAI) for building complex, multi-step reasoning chains.
• Planning & Memory: Experience implementing agentic "memory" (long-term/short-term) and planning strategies (like ReAct or Tree of Thoughts).
• Data Structures: Expert 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.