Member of Technical Staff, Machine Learning

Role overview Own the development and production operation of machine learning systems for AI-powered applications. The work includes classification, retrieval, ranking, and personalization, with a practical focus on shipping reliable models and improving their quality through evidence from evaluation and real-world use.

Responsibilities - Build and refine models for classification, ranking, retrieval, and personalization. - Improve search relevance and contextual retrieval using embeddings, hybrid search, and reranking methods. - Develop labeled datasets, evaluation pipelines, and metrics that measure model quality and catch regressions. - Maintain training, inference, and deployment pipelines so releases are robust and reproducible. - Monitor deployed models, diagnose production issues, and use feedback to guide iteration. - Fine-tune or adapt existing models when sufficient data is available, balancing accuracy, latency, cost, and reliability.

Requirements - Strong grounding in machine learning and modern neural architectures. - Experience building and deploying machine learning models in production. - Work with at least one relevant area such as classification, ranking, retrieval, recommendation, or personalization. - Understanding of evaluation, experimentation, and model performance metrics. - Ability to write production-quality code and work with real-world datasets. - Ownership, sound problem-solving, and ability to make practical quality and reliability tradeoffs.

Nice to have - Familiarity with Python, PyTorch or JAX, embeddings, vector search, ranking models, and production inference pipelines.

Benefits and work setup The working approach is described as hands-on and collaborative, with collective decisions, quick execution, and continuous learning. The interview process is expected to involve three or four rounds, virtual and/or onsite, with a prompt decision.