Engineering Manager - Applied ML; Search & Recommendations

Location

San Mateo, California

Full time

Parspec US

Founded in 2021, Parspec is revolutionizing material procurement for the $13 trillion USD construction industry by digitizing and organizing the industry's product data. Our proprietary AI technology maintains a current and comprehensive catalogue of millions of products, enabling our customers to identify products that best meet their needs - instantly. Trusted by top designers, builders, distributors and sales agents and backed by leading venture investors, Parspec is paving the way for a more innovative, connected, and sustainable future in construction.

Join us in building transformative technology that reshapes one of the world’s oldest and largest industries.

We are seeking a technical Engineering Manager, Applied ML (Search & Recommendations) to lead our Search Retrieval, Ranking and Recommendations. You will be the architect of our "Discovery Engine," moving beyond keyword matching to deep semantic understanding of construction data. You will lead a team of Applied ML engineers to design and deploy state-of-the-art models leveraging LLMs, vector databases, and sophisticated re-ranking algorithms to transform how the industry procures materials.

• Semantic Search & Ranking
  :
  Own the architecture for our hybrid search engine, blending keyword-based retrieval with dense vector embeddings to improve precision and recall.
• Recommendation Systems
  :
  Design and scale personalization algorithms that suggest products based on project specs, historical data, and cross-catalog compatibility.
• Model Fine-Tuning
  :
  Lead the fine-tuning of open-source and proprietary LLMs/encoders for specialized construction domain tasks, including NER and relationship extraction from complex documents.
• Vector Infrastructure
  :
  Architect and optimize our vector database strategy for high-concurrency retrieval and low-latency ranking.

• Mentorship
  :
  Lead, mentor, and grow a high-performing team of Machine Learning Engineers.
• Cross-functional Collaboration
  :
  Work closely with product managers, UX designers, and business leadership to integrate AI components into fully functional systems.
• Lifecycle Management
  :
  Participate in the complete product lifecycle from concept design to development, testing, and deployment.

• Performance at Scale
  :
  Build products that handle large data volumes efficiently while remaining highly scalable for new clients.
• MLOps
  :
  Design end-to-end data and ML pipelines for seamless production integration and monitoring.

• R&D Leadership
  :
  Work with the leadership team on research efforts to explore cutting-edge technologies.
• Engineering Standards
  :
  Uphold a culture of excellence by maintaining high standards in code quality, innovation, and rigorous experimentation.

Your contributions will be instrumental in advancing Parspec’s AI capabilities, enabling us to build intelligent systems that solve real-world problems in construction technology. By developing scalable AI solutions, you will help digitize an industry while driving innovation through state-of-the-art machine learning techniques.

You are a seasoned ML leader with a strong track record of building and managing technical teams in a dynamic environment. You are passionate about solving real-world discovery and extraction challenges using advanced AI

• Education
  :
  Bachelor’s or Master’s degree (PhD preferred) in Science or Engineering with strong programming and analytical skills.
• Leadership
  : 3+ years managing ML teams, with a track record of shipping production-grade search or recommendation products.
• Domain Expertise
  :
  Deep conceptual understanding and hands-on experience in Search, Ranking, Recommendation systems, or NLP/Document Extraction.
• Technical Proficiency
  :
  Expertise in Python (Num Py, scikit-learn, pandas) and training deep learning models using PyTorch or Tensor Flow.
• Software Excellence
  :
  Ability to drive high standards for clean, efficient, and bug-free code.

• Search & Ranking
  :
  Deep experience with Learning to Rank (LTR), BM25, and hybrid retrieval strategies.
• Vector DBs & Embeddings
  :
  Hands-on experience with Vector Databases (Pinecone, Qdrant, Milvus) and optimizing embedding spaces for domain-specific retrieval.
• Model Optimization
  :
  Expertise in fine-tuning Large Language Models (LLMs) and Bi-Encoders/Cross-Encoders for specialized semantic search.
• Advanced MLOps
  :
  Experience building evaluation frameworks for search (nDCG, MRR) and managing the lifecycle of embedding deployments.
• AI Agent Orchestration
  :
  Hands-on experience with agentic frameworks (e.g., Lang Graph, Auto Gen, or CrewAI) for building complex, multi-step reasoning chains.
• Research & Community: A track record of publications in top-tier conferences (e.g., NeurIPS, SIGIR, KDD, ACL) or significant…