Machine Learning Engineer, Applied AI

Bjak•Ireland, MS

About The Position

Build and improve machine learning systems that power AI-native applications. Own practical ML problems across model development and production deployment, including classification, retrieval, ranking, personalization, and evaluation. Improve model quality while shipping reliable systems for real users. Develop efficient classifiers; improve search relevance and context retrieval using embeddings, hybrid search, and reranking; build privacy-conscious personalization models; and create evaluation pipelines, datasets, and metrics. Fine-tune existing models when sufficient data is available. Build and maintain training, inference, and deployment pipelines, monitor production performance, debug issues, and use real-world feedback to guide improvements. The role works with Python, PyTorch or JAX, embeddings, vector search, ranking models, production ML systems, and inference pipelines. The team is described as small, hands-on, high-talent-density, and collaborative, with collective decision-making, rapid execution, continuous learning, and an expectation that team members bring structure, exercise judgment, and execute independently.

Requirements

Strong foundations in machine learning and modern neural architectures.
Hands-on experience building and deploying ML models in production.
Experience in classification, ranking, retrieval, recommendation systems, or personalization.
Understanding of model evaluation, experimentation, and performance metrics.
Ability to write production-quality code and work with real-world datasets.
Ability to balance model quality, latency, cost, and reliability.
Strong ownership and problem-solving skills.
Ability to bring structure, exercise judgment, and execute independently.

Responsibilities

Build and improve ML models for classification, ranking, retrieval, and personalization.
Develop efficient classifiers and optimize accuracy, precision, latency, and cost.
Improve search relevance and context retrieval with embeddings, hybrid search, and reranking.
Build personalization models that learn from user behavior while respecting privacy.
Develop evaluation pipelines, labeled datasets, and metrics to measure model quality.
Fine-tune and adapt existing models when sufficient data is available.
Build and maintain ML training, inference, and deployment pipelines.
Debug production issues, monitor model performance, and ship improvements based on real-world feedback.
Deliver production ML models that meet accuracy, relevance, latency, and reliability targets.
Create robust, reproducible, and maintainable training, inference, and data pipelines.
Collaborate with engineering and product teams to deliver reliable ML-powered features.