Senior Machine Learning Engineer. Job in San Mateo Move Collective Jobs

AI System Development

• Search & Recommendation Systems: Spearhead the design and creation of advanced Search, Ranking, and Recommendation systems to empower customers in navigating extensive technical product offerings.
• Document Extraction & NLP: Construct high-precision NLP and document extraction pipelines that efficiently digitize and structure complex construction data sourced from unstructured formats.
• Advanced Architecture: Investigate and deploy novel deep learning architectures, with a focus on hybrid retrieval models and meticulously fine-tuned LLMs.
• Production Deployment: Develop, train, and deploy scalable deep learning and machine learning models tailored for seamless integration into production environments.
• Agentic Workflows: Design autonomous or semi-autonomous agents capable of planning and executing multi-step discovery tasks.

Full Product Lifecycle Participation

• Technical Leadership: Partner with product managers and UX designers to weave AI components into fully functional systems, offering technical insight on feasibility and architecture.
• End-to-End Ownership: Engage throughout the entire product lifecycle—from conceptual design to development, integration, testing, and deployment.

Scalable Solutions

• High-Volume Data: Develop products that effectively manage large datasets while ensuring scalability for onboarding new clients.
• Pipeline Design: Architect comprehensive end-to-end data and ML pipelines, guaranteeing smooth integration and monitoring in production settings.

Research & Collaboration

• R&D Initiatives: Collaborate closely with leadership on research projects that delve into cutting-edge technologies such as vector databases and embedding-based retrieval.
• Excellence Standards: Foster a culture of engineering excellence by adhering to high standards in code quality, documentation, and innovation.

Minimum Qualifications

• Education: Bachelor's or Master's degree (PhD preferred) in Science or Engineering, with robust programming and analytical skills.
• Machine Learning Expertise: In-depth understanding of machine learning principles, particularly in NLP, Search, or Ranking.
• Technical Skills: Direct experience implementing ML projects in Python utilizing libraries like NumPy, scikit-learn, and pandas.
• Deep Learning: Skilled in training and fine-tuning deep learning models using frameworks such as PyTorch or TensorFlow.
• Leadership: Demonstrated ability to guide technical initiatives from conception to execution while navigating intricate challenges.

Preferred Qualifications

• Specialized Infrastructure: Extensive experience with Vector Databases (e.g., Pinecone, Milvus) and optimizing embedding models for retrieval tasks.
• Fine-tuning: Proficient in fine-tuning LLMs for specific domain assignments and ranking criteria.
• AI Agent Orchestration: Practical experience with agentic frameworks (e.g., LangGraph, AutoGen, or CrewAI) for constructing complex, multi-step reasoning tasks.
• Planning & Memory: Background in implementing agentic memory (long-term/short-term) and planning methodologies (like ReAct or Tree of Thoughts).
• Data Structures: Expert knowledge in algorithms and data structures.
• Research & Community Engagement: A track record of publications in prestigious conferences (e.g., NeurIPS, SIGIR, KDD, ACL) or significant contributions to open-source ML projects.