Top Hybrid Machine Learning Jobs
The Machine Learning Infra Engineer will bridge the gap between data science and production systems by optimizing models for runtime performance, designing scalable MLOps pipelines, and implementing monitoring frameworks. They will work closely with data scientists to improve workflows and ensure reliability across the system, while also developing data versioning and deployment strategies.
The ML Research Scientist will lead research projects enhancing the Genesis AI platform in generative modeling for molecular systems, conduct large-scale experiments, and mentor junior team members while contributing to the research community through publications.
As a Machine Learning Engineer, you will develop and deploy machine learning models to tackle complex business problems. Responsibilities include collaborating with cross-functional teams, analyzing data, optimizing models, and ensuring production-quality deployments while staying updated on machine learning advancements.
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In this role, you will lead various data and analytics projects, collaborating with client leaders to address business challenges using data-driven strategies. Responsibilities include designing and executing analytics strategies, mentoring junior staff, and supporting the growth of the consulting practice.
The Machine Learning Engineer will collaborate with research scientists and partner laboratories to develop and improve end-to-end ML pipelines for multimodal models. The role involves training and serving robot foundation models while enhancing data infrastructure and contributing to significant publications.
The AI scientist will modify and fine-tune large language models to improve human interaction, enable external tool calling, manage model alignment based on deployment feedback, and engage in model pre-training. Strong expertise in generative AI and MLOps is essential, along with proficiency in AI frameworks like TensorFlow and PyTorch.
As an AI Research Scientist, you will conduct applied research to advance machine learning techniques in energy markets, investigate new technologies, design solutions for unsolved problems, and publish findings in academic papers. Key responsibilities include developing mathematical understandings of energy systems, analyzing experiments, and implementing ML techniques.
Design and implement infrastructure for training, evaluating, and deploying deep learning models for self-driving semi trucks. Develop and optimize training pipelines, track metrics, and enable continuous integration and deployment of model improvements in collaboration with cross-functional teams.
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The Sr. Software Engineer for Machine Learning at Tinder will develop algorithms and recommendation systems that enhance user matches and optimize business metrics. This role involves high-impact contributions in a collaborative, innovative engineering environment.
As a Senior Software Engineer at Mashgin, you will leverage your expertise in machine learning and computer vision to design and implement deep learning algorithms. You will focus on solving real-world problems, develop innovative data collection methods, and transition research into production-ready solutions collaboratively with a small team.
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