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) (AI/ML infrastructure): Building reliable, scalable platforms for model training, evaluation, deployment, inference, and continuous improvement with an accent on high-throughput serving, low latency, observability, and cost efficiency. Focus on designing distributed ML systems, building reproducible pipelines, and detecting and diagnosing model and infrastructure regressions.
Location: Zurich, Switzerland; hybrid work arrangement.
Company
ActAI builds proactive AI-native applications for conversations, errands, organisation, and workflows, with a focus on reliable long-running tasks, persistent context, and real-world task completion.
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 optimise model-serving infrastructure for high-throughput and low-latency workloads.
- Develop reliable data, training, evaluation, model-release, and continuous-improvement pipelines.
- Build observability, monitoring, tracing, alerting, benchmarking, and evaluation tooling for AI/ML workloads.
- Work with AI engineers, researchers, and product engineers to deliver production-ready infrastructure.
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.
- Comfort working in ambiguous, fast-moving environments with ownership and continuous improvement.
Culture & Benefits
- Full-time hybrid employment in Zurich.
- Close collaboration with AI engineers, researchers, and product teams.
- Focus on experimentation, rapid delivery, reliability, scalability, and cost efficiency.
- Opportunity to build reusable ML platform primitives and evolve the AI stack as new models and inference techniques emerge.
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