MLOps/Infrastructure Engineer
Description For MLOps/Infrastructure Engineer
Mach9 is at the forefront of leveraging advanced machine learning and computer vision techniques to transform raw geospatial data into actionable insights to help civil engineers build and maintain infrastructure globally. Our first product, Mach9 Digital Surveyor, helps surveyors automatically extract features from large-scale imagery and 3D datasets over 30x faster than today's manual and labor-intensive drafting workflows, accelerating the development of cost-effective and sustainable transportation and utility infrastructure.
As an MLOps/Infrastructure Engineer at Mach9, you will:
- 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.
We're looking for candidates with:
- 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.
Join Mach9 to be part of a team that's revolutionizing the way civil engineers build and maintain infrastructure using cutting-edge machine learning and computer vision technologies.
Responsibilities For MLOps/Infrastructure Engineer
- 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
- Implement tracking tools to log experiments, monitor metrics, and enable reproducibility
- Develop and manage CI/CD pipelines for machine learning workflows
- Work closely with ML Engineers, Data Scientists, and DevOps to improve infrastructure and resolve technical issues
Requirements For MLOps/Infrastructure Engineer
- 3+ years of experience in MLOps, DevOps, or Infrastructure Engineering
- 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
- High degree of self-motivation and accountability
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