обновлено 27 дней назад
ML Platform Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
ML Platform Engineer (AI): Building infrastructure and platforms for training, evaluating, deploying, and serving AI models with an accent on reliability, scalability, latency, and cost efficiency. Focus on designing distributed ML systems, reproducible pipelines, production observability, and high-throughput inference infrastructure.
Location: Seoul, South Korea; hybrid
Company
Building proactive AI-native applications that help users manage conversations, errands, organization, and workflows with minimal prompting.
What you will do
- Build and operate ML infrastructure and platforms powering AI products.
- Design systems for model training, evaluation, deployment, inference, and experimentation.
- Build and optimize model-serving infrastructure for high-throughput and low-latency workloads.
- Develop data, training, evaluation, model-release, and continuous-improvement pipelines.
- Build evaluation, benchmarking, observability, monitoring, tracing, and alerting infrastructure for AI/ML workloads.
- Work with AI engineers, researchers, and product engineers to turn evolving model requirements into production-ready systems.
Requirements
- Strong software engineering fundamentals and experience building production systems.
- Experience with ML infrastructure, platforms, or production machine learning systems.
- Experience with model deployment, inference, evaluation, or data pipelines.
- Strong understanding of distributed systems and system reliability.
- Ability to write clean, maintainable, production-quality code.
- Experience or familiarity with Python, PyTorch or JAX, cloud infrastructure, ML pipelines, GPU infrastructure, and vector databases.
Culture & Benefits
- Full-time hybrid work in Seoul.
- Work in a fast-moving environment focused on ownership, experimentation, and continuous improvement.
- Build reusable ML platform primitives that support multiple AI products.
- Help evolve the AI stack as new models, architectures, and inference techniques emerge.
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