Machine Learning Engineer (M/W/D)

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. 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 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. PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.We are expanding our team to accelerate the development of AI applications. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information. #