AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the usage of frontier data and models by building complex agents for enterprises around the world through our Scale Generative AI Platform (SGP). The SGP ML team works on the front lines of this AI revolution. We interface directly with clients to build cutting edge products using the arsenal of proprietary research and resources developed at Scale. As an ML Engineer, you’ll work with clients to train ML models to satisfy their business needs. Your work will range from training next-generation AI cybersecurity firewall LLMs to training foundation genomic models making predictions about life-saving drug proteins. Having a deep curiosity about the hardest questions about LLMs will also motivate various research opportunities on how to apply ML to the forefront of enterprise data. If you are excited about shaping the future of the modern AI movement, we would love to hear from you! Train state of the art models, developed both internally and from the community, in production to solve problems for our enterprise customers. Work with product and research teams to identify opportunities for ongoing and upcoming services. Work closely with customers - some of the most sophisticated ML organizations in the world - to quickly prototype and build new deep learning models targeted at multi-modal content understanding problems. At least 1-3 years of model training, deployment and maintenance experience in a production environment Solid background in algorithms, data structures, and object-oriented programming AWS or GCP) and developing machine learning models in a cloud environment Experience building products with LLMs including knowing the ins and outs of evaluation, experimentation, and designing solutions to get the most of the models PhD or Masters in Computer Science or a related field Experience in dealing with large scale AI problems, ideally in the generative-AI field Demonstrated expertise in large vision-language models for diverse real-world applications, e.G. classification, detection, question-answering, etc. Published research in areas of machine learning at major conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) Strong high-level programming skills (e.G., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc.
