3 дня назад
MLOps 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
MLOps Engineer (AI): Optimizing, deploying, and operating large-scale machine learning models for molecular modeling and design across distributed systems with an accent on training and inference performance, multi-GPU scaling, and production reliability. Focus on profiling workloads, reducing memory and compute overhead, improving throughput and latency, and turning research models into robust production services.
Location: London, with remote and in-office work options
Company
is a public benefit company building AI-powered molecular modeling tools and production-grade platforms to make biology programmable and accelerate drug discovery.
What you will do
- Optimize, deploy, and operate large-scale machine learning models powering Lab.
- Turn trained research models into reliable, production-ready services in collaboration with ML researchers.
- Profile and improve training and inference performance, including throughput, latency, memory usage, and compute efficiency.
- Scale workloads across distributed systems, multi-GPU environments, and cloud infrastructure.
- Harden ML systems for long-running, high-volume workloads and real-world scientific use.
Requirements
- 5+ years of industry experience.
- Strong experience deploying and operating machine learning models in production.
- Experience optimizing training and inference workloads and working with distributed frameworks and tooling.
- Hands-on experience with PyTorch and the scientific Python ecosystem.
- Strong understanding of MLOps practices, including experiment tracking, model versioning, reproducibility, and CI/CD for ML systems.
- Strong software engineering fundamentals and experience building reliable, well-tested, maintainable ML infrastructure.
Nice to have
- Exposure to computational biology or chemistry workflows and data formats.
- Experience with large-scale scientific or numerical workloads.
- Experience operating ML systems under constraints involving cost, latency, and reliability.
Culture & Benefits
- Opportunity to build tools used by thousands of scientists across biotech, academia, and industry.
- Significant ownership and independence, with responsibility for driving projects from concept to deployment.
- Highly competitive salary with substantial equity ownership.
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