Applied Machine Learning Engineer

Role overview Build and improve the machine learning systems behind AI-powered applications, taking practical problems from model development through production deployment. The work centers on improving classification, retrieval, ranking, and personalization so that product features deliver relevant, dependable results for real users.

Responsibilities - Develop and refine models for classification, ranking, retrieval, and personalization. - Improve search and context retrieval with embeddings, hybrid search, and reranking approaches. - Create personalization methods that learn from user behavior while accounting for privacy. - Build evaluation pipelines, labeled datasets, and metrics to assess model quality and catch regressions. - Maintain training, inference, and deployment pipelines, adapting existing models when data supports it. - Monitor production behavior, investigate issues, and ship improvements based on measured results. - Work with engineering and product partners to deliver reliable machine learning features.

Requirements - Strong foundations in machine learning and modern neural network architectures. - Hands-on experience building and deploying machine learning models in production. - Experience with at least one relevant area, such as classification, ranking, retrieval, recommendations, or personalization. - Understanding of model evaluation, experimentation, and performance metrics. - Ability to write production-quality code and work with real-world datasets. - Sound judgment balancing model quality, latency, cost, and reliability, with strong ownership and problem-solving skills.

Nice to have The stated technical environment includes Python, PyTorch or JAX, embeddings, vector search, ranking models, and production inference systems. The team describes itself as small and hands-on, with collective decision-making, fast iteration, and a focus on learning from results.