Applied Artificial Intelligence Engineering Manager, Scale AI

• If you are excited about shaping the future of the modern AI movement, we would love to hear from you!
• At least 3 years of model training, deployment and maintenance experience in a production environment
• At least 1-2 years of management or tech leadership experience
• Strong skills in NLP, LLMs and deep learning
• Solid background in algorithms, data structures, and object-oriented programming
• Experience working with a cloud technology stack (eg. 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

Desirable

• 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 AAI Engineering Manager, you’ll manage a team of high-calibre Applied AI Engineers + MLEs who work with clients to train ML models to satisfy their business needs
• Your team’s work will range from training next-generation AI cybersecurity firewall LLMs to training foundation agentic action models making predictions about business-saving outcomes
• You will guide your team towards using data-driven experiments to provide key insights around model strengths and inefficiencies in an effort to improve products
• Train state of the art models, developed both internally and from the community, in production to solve problems for our enterprise customers
• Manage a team of 5+ Applied AI Engineers / ML Engineers
• 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
• Create state of the art techniques to integrate tool-calling into production-serving LLMs
• 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