MLOps/Infrastructure Engineer

2 weeks ago

San Francisco, United States Mach9 Robotics Inc Full time
About Mach9

Mach9 is at the forefront of leveraging advanced machine learning and computer vision techniques to transform raw geospatial data into actionable insights, empowering civil engineers to design, build, and maintain infrastructure worldwide. Our first product, Mach9 Digital Surveyor, helps surveyors automatically extract features from large-scale imagery and 3D datasets 96x faster than today's manual and labor-intensive drafting workflows, accelerating the development of cost-effective and sustainable transportation and utility infrastructure. Mach9 supports leading asset owners and engineering and construction organizations globally to solve the toughest engineering design, mission planning, and asset management problems.

Mach9 is backed by Quiet Capital, Y Combinator, Overmatch Ventures, Kyle Vogt (founder of Cruise), Amar Hanspal (former CEO of Autodesk), Scott Belsky (CPO of Adobe), Gokul Rajaram (former executive at DoorDash), and more.
What you'll do:
  • Design, implement, and maintain robust, scalable pipelines to streamline model training, evaluation, and deployment processes.
  • Diagnose, troubleshoot, and resolve CUDA driver and GPU-related issues, optimizing resource allocation to support efficient model training and inference.
  • Implement tracking tools to log experiments, monitor metrics, and enable reproducibility across model iterations and architecture changes.
  • Develop and manage CI/CD pipelines specifically for machine learning workflows, ensuring reproducibility, reliability, and version control across environments.
  • Work closely with ML Engineers, Data Scientists, and DevOps to improve infrastructure, resolve technical issues, and ensure seamless deployment workflows.
What we're looking for:
  • 3+ years of experience in MLOps, DevOps, or Infrastructure Engineering, with a strong understanding of machine learning workflows and deployment best practices.
  • Strong knowledge of ML deployment and runtime technologies, including CUDA.
  • Experience in deploying and managing machine learning models in production environments.
  • Ability to independently manage and track progress on projects, demonstrating a high degree of self-motivation and accountability.

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