About the Role
Title: AI Research Scientist II, LLM
Location: United States
Job Description:
Join Axon and be a Force for Good.
At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other.
Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter.
Your Impact
Do you want to contribute to the work that addresses some of today’s most challenging problems in the public safety space? Are you passionate about AI and Machine Learning? Are you looking for an opportunity to train machine learning models and deploy it in real life situations for the greater good?
As a Research Scientist II, you will investigate the research approach, assess the implementation risk and define the success metrics for multiple AI/ML projects while collaborating with other research scientists and machine learning engineers. You will be actively involving the entire AI innovation life cycle from model prototyping to deployment and continuous learning.
The ideal candidate will have a proven scientific background and a consistent track record of successfully researching and delivering scalable AI-based products with hands-on execution. We need someone willing to be fearless and more than willing to take on bold challenges. By accelerating adoption of our technologies, you’ll help protect life in public safety for both officers and the communities they serve around the world.
What You’ll Do
- 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.
What you Bring
- A Master’s Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics or an equivalent highly technical field
- 3+ years of combined academic and industrial research experience developing LLM and other NLU models.
- Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline.
- 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.
- 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 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.
- Track record of publications and contributions to the machine learning community.
- Experience with designing and shipping software products that leverage machine learning at scale.
- 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.
Preferred
- A Ph.D. Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics or an equivalent highly technical field.
- Be familiar with privacy-preserving ML and ethical AI techniques.
- Demonstrated knowledge and experience with distributed machine learning and deploying models at scale in cloud environments (such as AWS, Microsoft Azure and Google Cloud).
- Familiarity with IoT/Edge AI and optimizing ML models to run on-device with constrained compute, power and latency budgets.
- Familiarity with multi-modal AI development.