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Staff Machine Learning Engineer

Diligente Technologies • San Mateo, CA

Mid-Senior level Posted April 17, 2026 Job link

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Responsibilities

Commitments

Responsibilities

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

Not Met Priorities

What still needs stronger evidence

Requirements

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
Excellence Standards : Uphold a culture of engineering excellence by maintaining high standards in code quality, documentation, and innovation Minimum Qualifications
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 Skills

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.

Education

(Not required) – Education : Bachelor’s or Master’s degree (PhD preferred) in Science or Engineering with strong programming and analytical skills.

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.