- Locations
- London
- United States
- Last Published
- Dec. 15, 2024
- Sector
- AI/ML
- Function
- Software Engineering
Scale is growing rapidly, and joining the International Public Sector team is an opportunity to work on one of the most rapidly expanding teams at Scale. This team generates, executes, and fosters Scale’s work outside the United States. There are three core types of work involved:
- Building custom models
- Providing high-quality training data for research institutions building LLMs from scratch
- Partnerships, upskilling, and advisory
As a Machine Learning Research Engineer, you'll be at the forefront of building models as a service, transforming real-world business problems through cutting-edge computer vision and large-vision language models. This is an extraordinary opportunity to take full ownership and make a significant impact in the data-centric AI movement.
We are looking for individuals who are passionate about AI, eager to drive innovation, and excited by ambitious challenges. Join us in revolutionizing government operations with AI and work on production use cases that truly harness the power of AI models to solve complex problems.
You will:
- Dive into and implement groundbreaking research in computer vision and large vision-language models.
- Design and build agent workflows that leverage customized models for downstream tasks, including hybrid solutions with LLMs.
- Translate customer needs into innovative AI solutions tailored for government applications.
- Work with vast, unstructured visual and multimodal datasets to develop robust models.
- Create and maintain robust evaluation systems.
- Collaborate closely with our data annotation teams to train models on high-quality datasets.
- Travel one week monthly to engage with customers and understand their unique challenges firsthand.
Minimum Qualifications:
- At least 2+ years of model training, deployment and maintenance experience in a production environment.
- Strong skills in computer vision and/or large vision-language models.
- Willing and able to travel to client sites in the Middle East for one week each month.
Ideal Qualifications:
- Strong skills in NLP, LLMs, and deep learning.
- Experience working at a startup or in a forward-deployed role.
- Strong written and verbal communication skills to operate in a cross-functional team environment.
- Proficient in reading and writing in Arabic.
- Experience in dealing with large-scale AI problems.
- Published research in areas of machine learning at significant conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals.
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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