Machine Learning Engineer - Search & Recommendation
Role summary
We are seeking a Sr. Machine Learning Engineer specializing in Search and Recommendation to join our team. This full-time, direct-hire role requires a strong understanding of Python and PyTorch, with expertise in data handling, ML pipelines, LLMs, prompt tuning, and fine-tuning models. You will work on ranking and recommendation systems, leveraging Vector DBs and embeddings, and have experience taking ML models into production. Experience with multi-agent and agentic workflows is also key. Preferred qualifications include deep experience with specialized infrastructure like Vector Databases (Pinecone, Milvus), optimizing embedding models, AI agent orchestration frameworks (LangGraph, AutoGen, CrewAI), implementing agentic memory and planning strategies, expert knowledge of algorithms and data structures, and a track record of publications or open-source contributions.
Job Title: Sr. Machine Learning Engineer - Search and Recommendation
Duration: Full-Time/Direct Hire
Location: San Mateo, CA
Must have: Experience with Search and Recommendation
Key Skills
- Python (strong), PyTorch
- Data handling / ML pipelines
- LLMs, prompt tuning
- Fine-tuning models
- Vector DB & embeddings
- Experience taking ML models into production
- Work on ranking & recommendation systems
- Multi-agent / agentic workflows
- Build production-ready ML solutions
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.