onsite
AI Engineer/Researcher
AI Engineer/Researcher
The AI Engineer/Researcher will drive the development of GenAI software and products, focusing on LLM applications from research to deployment. This role involves engineering LLM applications, managing the ML lifecycle, and collaborating with cross-functional teams to build cutting-edge AI solutions.
About the role
About the Role
We are seeking a highly skilled and motivated AI engineer/researcher to join our innovative team. The ideal candidate will have a strong background in modern LLM architectures and applications, and experience in using GenAI approaches in an applied, production environment.
Responsibilities
- Research: Follow and understand recent trends in GenAI developments, and how they may apply to directions the company is pursuing.
- Engineering: Design, build, and maintain LLM applications; own the ML lifecycle, including data analysis and processing, model architecture and training, metrics/evaluation, and MLOps.
- Communication: Convey complex technical information at different levels, including to other LLM experts and non-AI practitioners.
- Collaboration: Work closely with product engineers, designers, and data scientists to define and build cutting-edge AI solutions across the company.
Requirements
- Education: Bachelor's or Master's degree in Computer Science, Engineering, Math, or a related field; Ph. D. or similar experience is a plus.
- Experience: 10+ years of experience in the ML space; track record of deploying AI models in real-world applications.
- Skills: Excellent coding skills; proficiency in Python and familiarity with libraries such as PyTorch, TensorFlow, NumPy, and JAX. Strong understanding of machine learning algorithms and data structures.
- Communication: Excellent communication and presentation skills, with the ability to clearly articulate design decisions and rationale.
- Problem-Solving: Strong analytical skills with the ability to work independently and collaboratively in a fast-paced environment.
Preferred Qualifications
- Publications: Authored or co-authored research papers in reputable AI/ML venues, or impactful technical blog posts.
- Open source: Active participation in open-source relevant repos, Kaggle competitions, and similar projects.
- Startup experience: Worked in small, fast-paced environments.