Machine Learning Engineer, Applied AI

Role overview

Build and operate machine learning models that help a mobile communication product make conversations, coordination, and everyday workflows more useful with little or no prompting. The work spans classification, search and retrieval, ranking, and personalization, with a focus on delivering reliable models that perform well in production.

Responsibilities

- Develop and improve models for classification, retrieval, ranking, and personalization. - Tune model quality alongside serving needs such as latency, inference cost, and reliability. - Improve search relevance and context retrieval using embeddings, vector search, hybrid retrieval, or reranking techniques. - Build evaluation datasets, pipelines, and metrics to track quality and spot regressions. - Train, fine-tune, deploy, and monitor models through production ML workflows. - Investigate production issues and use evaluation results and user feedback to guide improvements.

Requirements

- At least three years of machine learning engineering experience, including production model deployment. - Strong Python skills and experience with PyTorch or JAX. - Sound understanding of ML algorithms, model training, and evaluation. - Experience with classification, ranking, retrieval, recommendation, or personalization models. - Familiarity with embeddings, vector search, or information retrieval systems. - Experience building data pipelines, training workflows, and model inference services. - Ability to assess model performance through appropriate metrics and experimentation, and weigh quality against latency, cost, and reliability.

Nice to have

- Experience with transformer models, large language model fine-tuning, or embedding models. - Familiarity with hybrid search, reranking, and retrieval evaluation. - Experience with recommendation or personalization in consumer products, or large scale ML training and inference.