7 months ago
San Francisco, CA, USAMid Level / Senior
Responsibilities
- Design and implement multi-stage matching systems for compatibility scoring and personalization.
- Develop and maintain ML pipelines for data ingestion and model training.
- Prototype workflows for natural-language and voice interactions.
- Deploy and monitor ML models in production with performance guardrails.
- Run experiments to measure engagement and match success rates.
- Collaborate with engineers and designers to integrate AI into the user experience.
Requirements
- 3+ years in applied ML or data science engineering roles, focusing on recommendation or personalization systems.
- Strong proficiency in Python and modern ML frameworks like PyTorch and TensorFlow.
- Experience with LLMs, embeddings, and agentic workflows.
- Understanding of A/B testing and human-in-the-loop system design.
- Familiarity with ANN search systems and MLOps tools is a plus.
- Reinforcement learning or preference modeling experience is a strong plus.
- Commitment to building safe, fair, and human-centered AI experiences.