The company is looking to address challenging problems in the public safety space using AI and Machine Learning.
Requirements
- Hands on experience in developing, scaling and implementing machine learning solutions using relevant programming languages (such as Python), state-of-the-art deep learning frameworks (such as PyTorch and Tensorflow) and code development and review tools (such as Github).
- Experience in big data ML as well as data efficient ML that leverages techniques such as synthetic data construction, transfer learning, active learning, semi-supervised learning, few-shot learning.
- Experience in prompt engineering.
- Experience in finetuning ML models.
- Experience in developing LLM-based applications including agent-based systems, RAG-based systems.
- Be Familiar with NLU/LLM cloud services and APIs (such as from OpenAI).
- Deep understanding of metrics for offline and online evaluation of LLM-based systems.
Responsibilities
- Drive one or more phases in ML development: shape datasets, investigate ML architectures, train/evaluate/tune ML models, implement end-end pipeline.
- Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale.
- Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges.
Other
- A Master’s Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics or an equivalent highly technical field
- 5+ years of combined academic and industrial research experience developing LLM and other NLU models.
- Excellent problem solving skills and ability to dive into learning optimization, model architecture, evaluation metrics, and field testing scenarios.
- Comfort communicating and interacting with scientists, engineers and product managers as well as understanding and translating the science of AI and Machine Learning to a more general audience.
- A Ph.D. Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics or an equivalent highly technical field (Preferred)
- Flexible working hours
- Opportunities for training and rotations in the US
- Opportunities to ride along with real US police officers in real life situations, see them use technology, and get inspired
