About the Company: We are a well-funded, rapidly growing, early-stage startup building a global consumer AI product for daily needs. We work on the cutting edge of AI across the stack. Check out our product and how we are championing the AI landscape by introducing every AI to everyone on our launch blog post. Moreover, the consumer product is built for the deeper purpose of solving the foundational challenge of robust and trustworthy AI model evaluations and data.
Job Summary: We are seeking a highly skilled and motivated AI engineer 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.

QUALIFICATIONS:

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: 3+ years of experience in ML; 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 at small, fast-paced environments.
If you are passionate about advancing AI technologies and thrive in a dynamic, collaborative environment, we would love to hear from you. Apply today and join our team of talented individuals

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.