Engineering Manager - Applied ML (Search & Recommendations)
About the position 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.
Responsibilities
- 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.
Requirements
- 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 (NumPy, scikit-learn, pandas) and training deep learning models using PyTorch or TensorFlow.
- Software Excellence: Ability to drive high standards for clean, efficient, and bug-free code.
Nice-to-haves
- 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., LangGraph, AutoGen, 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 contributions to open-source ML projects.
- Experience working with geographically distributed teams across multiple time zones.
Benefits
- Competitive salary and benefits, including family insurance coverage, free health teleconsultations, and learning/upskilling budgets
- Equity in the company
- Flexible hours and a hybrid work setup
- Unlimited PTO
- Opportunity to grow with a fast-scaling company transforming a large market
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