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 MLRE your focus will 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 quarter 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 quarter
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
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
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.S. Army and U.S. Air Force, and enterprises including GM and Accenture. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an affirmative action employer and inclusive and equal opportunity workplace. 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 accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
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