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4 дня назад

Software Engineer II (ML Ops)

121 000 - 157 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR

Software Engineer II (ML Ops) (ML Platform): Developing reliable, scalable, and observable systems for model training, evaluation, and deployment for protein design with an accent on GPU cluster optimization and large-scale inference. Focus on translating research workflows into production-grade systems and building robust ETL data pipelines.

Location: Somerville, MA

Salary: $121,000 – $157,000 USD

Company

Generate:Biomedicines is a therapeutics company pioneering Generative Biology to computationally create breakthrough medicines.

What you will do

  • Build and extend core services for model training, evaluation, and large-scale inference across the ML platform.
  • Develop and maintain data pipelines and infrastructure, including ETL workflows and data warehousing systems.
  • Optimize and scale workloads across GPU clusters to improve resource utilization and cost efficiency.
  • Collaborate with scientists, engineers, and product managers to define requirements and system improvements.

Requirements

  • Must be located in or able to work from Somerville, MA.
  • 2+ years of experience in software or ML engineering, specifically for ML workloads.
  • Strong proficiency in Python and a solid understanding of software design principles.
  • Experience with distributed systems and containerized deployments on AWS using ECS or Kubernetes.
  • Familiarity with relational databases such as PostgreSQL or Redshift and ETL processes.
  • Experience with CI/CD, observability, and infrastructure as code tools.

Nice to have

  • Experience using AI-assisted development tools (e.g., code copilots, LLM-based workflows) to improve productivity.

Culture & Benefits

  • Competitive benefits package.
  • Annual bonus and equity compensation.
  • Opportunity to work at the intersection of machine learning, biological engineering, and medicine.

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