Scale is growing rapidly, and joining the Global International Public Sector team is an opportunity to work on one of the most rapidly expanding teams at Scale. This team is responsible for generating, executing, and fostering Scale’s work outside of the United States. There are three core types of work involved:
- Building custom LLMs
- Providing high-quality training data for research institutions building LLMs from scratch
- Partnerships, upskilling, and advisory
As the first MLE hire on the team you will focus be on developing Models as a Service for our 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:
- Study and implement cutting edge research in the field
- Design and implement agent workflows that leverage pre-training and fine tuning techniques to customize LLMs and embedding models for downstream tasks
- Understand customer needs
- Work with large unstructured data
- Build evaluation systems
- Work cross functionally with our data annotation teams and fine tune models on this data
- Travel up to 2 weeks per month to meet with the customer
Minimum Qualifications:
- At least 2+ years of model training, deployment and maintenance experience in a production environment
- Trained deep learning models + have built up that skillset
- Strong skills in NLP, LLMs and deep learning
- Ability and interest in traveling to the client site in the Middle East region at least one week each month
Ideal Qualifications:
- Proficient in reading and writing in Arabic
- Past experience working at a startup or in a forward-deployed role
- Has experience working cross functionally with operations
- 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
