Scale’s rapidly growing International Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. Our core work consists of:
- Creating custom AI applications that will impact millions of citizens
- Generating high-quality training data for national LLMs
- Upskilling and advisory services to spread the impact of AI
We are hiring multiple Machine Learning Engineers who will leverage the latest research to design, train, deploy, and evaluate computer vision and/or language models to power the custom applications we build for our public sector clients.
At Scale, we’re not just building AI solutions—we’re enabling the public sector to transform their operations and better serve citizens through cutting-edge technology. If you’re ready to shape the future of AI in the public sector and be a founding member of our team, we’d love to hear from you.
You will:
- Partner with public sector counterparts to deeply understand their challenges
- Apply the latest research as you design custom models or chains of models
- Collaborate closely with our data annotation teams to create high-quality training datasets
- Train, optimize, and deploy models into challenging production environments
- Develop and maintain robust evaluation systems
- Participate in customer engagements, including occasional travel (approximately one week per quarter)
- Contribute to the scaling of AI capabilities in the public sector through hands-on knowledge sharing
Ideally, you’d have:
- 5+ years of computer vision and/or language model training, deployment, evaluation, and maintenance experience in a production environment
- Master’s or equivalent work experience
- Software engineering experience with demonstrated proficiency in Python, TypeScript/JavaScript, C++ or similar
Nice to haves:
- Experience working at a startup
- Experience dealing with large-scale AI problems
- Strong written and verbal communication skills to operate in a cross-functional team environment
- Proficiency in Arabic (if focused on language models)
- Published research in areas of machine learning at significant conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals.
