Oxipital AI
Machine Learning Operations Engineer
Oxipital AI - Bedford, Massachusetts, us, 01730
Work at Oxipital AI
Overview
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Overview
Oxipital AI is on a mission to revolutionize the manufacturing industry with its cutting-edge AI-enabled machine vision solutions. These solutions drive greater resilience, operational efficiency, and sustainability in the most complex and critical manufacturing processes. As a fast-growing company striving to make a difference every day, we are seeking a Machine Learning Operations Engineer to help build and maintain our cutting-edge machine learning pipeline and the critical tools and infrastructure that support it. This role entails a particular focus on cloud-based pipeline infrastructure, CI/CD, deep neural network architectures, cloud-based model training, data management, and inference-time optimization. You will work on a variety of customer-focused projects throughout the product development life cycle, from initial proofs of concept through robust production-ready implementations. You'll work in a small group of machine learning scientists collaborating with the core side software group, and others in our cross-functional team.
The ideal candidate will have 2-4 years of professional experience designing and implementing high-performance software products in a production environment, and is comfortable working in a fast-paced dynamic environment. We are looking for hands-on work experience in several of the following areas: ML model training using AWS resources, CI/CD pipelines for machine learning, vision-based deep learning, edge deployment, neural network architecture design, traditional computer vision, 3D graphics and simulation, robotics, and full-stack development.
Primary Responsibilities:
Expand the capabilities of our machine learning model pipeline with new features around model training infrastructure, model lifecycle tracking, automated model evaluation, and data management. Use best practices to minimize the cost footprint of model development. Optimize the efficiency of our machine learning models at training and inference time. Design, develop, and maintain tools and infrastructure for training, deploying, and evaluating vision-based machine learning models. Contribute to a robust and scalable product pipeline. Requirements:
Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or related technical field; graduate degree preferred. 2 years of professional software development experience. Experience with training and deploying machine learning models using cloud-based resources. Experience with deep neural networks for computer vision applications preferred. Experience with AWS services such as EC2, S3, EKS, and SageMaker. Experience with Infrastructure as Code frameworks like TerraForm preferred. Strong proficiency in Python, particularly with libraries like PyTorch, NumPy, or OpenCV. Experience with software development and deployment in a Linux environment. A solid foundation of software development best practices such as issue tracking, static code checking, and automated testing preferred. Experience with full-stack software development preferred. Strong mathematical and analytical skills. Excellent written and verbal communication skills. Ability to work both independently and collaboratively on a cross-functional team. Strong attention to detail.
The ideal candidate will have 2-4 years of professional experience designing and implementing high-performance software products in a production environment, and is comfortable working in a fast-paced dynamic environment. We are looking for hands-on work experience in several of the following areas: ML model training using AWS resources, CI/CD pipelines for machine learning, vision-based deep learning, edge deployment, neural network architecture design, traditional computer vision, 3D graphics and simulation, robotics, and full-stack development.
Primary Responsibilities:
Expand the capabilities of our machine learning model pipeline with new features around model training infrastructure, model lifecycle tracking, automated model evaluation, and data management. Use best practices to minimize the cost footprint of model development. Optimize the efficiency of our machine learning models at training and inference time. Design, develop, and maintain tools and infrastructure for training, deploying, and evaluating vision-based machine learning models. Contribute to a robust and scalable product pipeline. Requirements:
Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or related technical field; graduate degree preferred. 2 years of professional software development experience. Experience with training and deploying machine learning models using cloud-based resources. Experience with deep neural networks for computer vision applications preferred. Experience with AWS services such as EC2, S3, EKS, and SageMaker. Experience with Infrastructure as Code frameworks like TerraForm preferred. Strong proficiency in Python, particularly with libraries like PyTorch, NumPy, or OpenCV. Experience with software development and deployment in a Linux environment. A solid foundation of software development best practices such as issue tracking, static code checking, and automated testing preferred. Experience with full-stack software development preferred. Strong mathematical and analytical skills. Excellent written and verbal communication skills. Ability to work both independently and collaboratively on a cross-functional team. Strong attention to detail.