pythonmachine learningpytorchstartupclassificationjaxpersonalizationretrievalrankingvector searchproduction ml
Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Hirify AI/Оригинал
TL;DR
Machine Learning Engineer (AI): Building and deploying ML systems for AI-native communication applications with an accent on classification, retrieval, ranking, personalization, and evaluation. Focus on improving model quality, developing reliable training and inference pipelines, and balancing accuracy, latency, cost, and production reliability.
Location: Remote from Ireland
Company
Building AI-native applications that bring proactive intelligence to communication, coordination, errands, and workflows with minimal prompting.
What you will do
Build and improve ML models for classification, ranking, retrieval, recommendation, and personalization.
Develop efficient classifiers and optimize accuracy, precision, latency, and cost.
Improve search relevance and context retrieval using embeddings, vector search, hybrid search, and reranking.
Develop evaluation pipelines, labeled datasets, and metrics to measure model quality and identify regressions.
Build and maintain training, inference, and deployment pipelines, while monitoring production performance and resolving issues.
Collaborate with engineering and product teams to deliver reliable ML-powered features.
Requirements
Strong foundations in machine learning and modern neural architectures.
Hands-on experience building and deploying ML models in production.
Experience with classification, ranking, retrieval, recommendation systems, or personalization.
Proficiency in Python and PyTorch or JAX.
Understanding of model evaluation, experimentation, and performance metrics.
Ability to write production-quality code and balance model quality, latency, cost, and reliability.
Culture & Benefits
Work remotely from Ireland; interviews are conducted virtually and/or onsite.
Small, high-talent-density, hands-on team with collective decision-making.
Fast execution balanced with high-quality work and continuous learning.
Independent ownership, structured judgment, and practical problem-solving are valued.
Technical team members evaluate applications, with three to four interviews and a prompt decision process.
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