Machine Learning Infrastructure Specialist

1 day ago


Philadelphia, Pennsylvania, United States Dnovo Labs Full time
Job Title: Machine Learning Infrastructure Engineer

At Dnovo Labs, we're pushing the boundaries of drug delivery through innovative artificial intelligence. Our team applies cutting-edge machine learning techniques to accelerate the discovery and optimization of novel delivery systems. By seamlessly integrating AI-driven predictions with experimental validation, we're revolutionizing the field of drug delivery and therapeutics.

Role Overview:

As a Machine Learning Infrastructure Engineer, you will be responsible for designing and developing advanced ML pipelines and managing the robust infrastructure that powers our algorithms. Your role involves architecting and maintaining scalable, distributed systems, optimizing cloud resources, and ensuring the reliability of our computational environment. You'll work on building efficient data processing pipelines, while also focusing on infrastructure management tasks such as capacity planning, performance tuning, and implementing DevOps practices.

Key Responsibilities:
  • Design, implement, and maintain state-of-the-art machine learning pipelines for biological simulations.
  • Manage and optimize large-scale distributed computing infrastructure, including cloud resources and on-premises systems.
  • Implement DevOps practices to improve deployment, monitoring, and maintenance of ML systems.
  • Optimize inference and training workloads across our computational environment.
  • Develop tools and frameworks to enhance the efficiency and reproducibility of our ML experiments.
  • Collaborate with ML researchers to translate their algorithms into production-ready code.
  • Implement robust monitoring, logging, and alerting systems for our entire ML infrastructure.
  • Continuously improve the scalability, performance, and cost-effectiveness of our distributed systems.
  • Stay up-to-date with the latest advancements in ML infrastructure and incorporate new technologies as appropriate.
Requirements:
  • 3-5+ years of experience building core infrastructure.
  • Experience running inference clusters at scale.
  • Experience operating orchestration systems such as Kubernetes at scale.
  • Pride in building and operating scalable, reliable, secure systems.
  • Experience with DevOps.
  • Ability to work well with both business and research-oriented team members.


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