Machine Learning Research Engineer

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 team focuses on building generative ML solutions for some of the largest companies in the world. 

Your focus will be on partnering closely with cutting edge research institutes and publishing new advancements in ML and LLMs. You will be involved end-to-end in the research process, and be embedded on site with other ML researchers. 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 
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals
• Solid background in algorithms, data structures, and object-oriented programming
• Native fluency in speaking, writing, and reading Arabic

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. 
• 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