Machine Learning Engineer, Enterprise Generative AI Platform

Performant model code, high quality data, and robust evaluation methods form the foundation of an AI system. Scale’s leading end-to-end solutions for the ML lifecycle based on real-world data will continue to set the bar for the data-centric AI movement. Scale’s Enterprise Generative AI team focuses on building generative ML solutions for some of the largest companies in the world. 

Your focus will be on developing Models as a Service for our enterprise partners by optimizing LLMs through finetuning, RAG or other techniques. You will be involved end-to-end from coordinating with operations to create high quality datasets to productionizing models for our customers. If you are excited about shaping the future of the data-centric AI movement, we would love to hear from you!

You will: 

• 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.
• Explore approaches that integrate human feedback and assisted evaluation into existing product lines. 
• 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. 

Ideally you’d have:

• At least 3 to 5 years of model training, deployment and maintenance experience in a production environment
• Strong skills in NLP, LLMs and deep learning 
• Solid background in algorithms, data structures, and object-oriented programming
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment

Nice to haves:

• 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.) and/or journals
• Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kuberflow, TensorFlow, etc. 
• Strong written and verbal communication skills to operate in a cross functional team environment