4 дня 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): Build and operate the infrastructure behind AI products, from model training and evaluation to deployment, inference, and observability, with an accent on reliability, scalability, latency, and cost efficiency. Focus on designing distributed ML platforms, optimizing high-throughput inference, and building reproducible pipelines that detect regressions and support rapid model iteration.
Location: Remote in the United States
Company
is building A1, a proactive AI assistant for conversations, errands, organization, and everyday workflows.
What you will do
- Build and operate ML infrastructure and platforms that power AI products in production.
- Design systems for model training, evaluation, deployment, inference, and experimentation.
- Optimize model serving and inference infrastructure for high-throughput and low-latency workloads.
- Develop reliable data, training, evaluation, model release, and continuous-improvement pipelines.
- Build benchmarking, observability, monitoring, tracing, and alerting infrastructure for AI/ML workloads.
- Collaborate with AI engineers, researchers, and product engineers to turn evolving model requirements into scalable production 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, reliability, and system performance.
- Production-quality Python development and the ability to write clean, maintainable code.
- Must be based in the United States.
Culture & Benefits
- Fast-moving environment focused on ownership, experimentation, and continuous improvement.
- Work closely with AI engineers, researchers, and product teams.
- Build reusable ML platform primitives instead of duplicating infrastructure for each AI product.
- Help evolve the AI stack as new models, architectures, and inference techniques emerge.
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