Description:

Inworld is seeking Staff and Principal level AI Researchers with extensive experience in researching, designing, and implementing machine learning models and algorithms.
The role involves advancing AI capabilities by developing state-of-the-art models and contributing to cutting-edge research in artificial intelligence.
Responsibilities include conducting research to advance AI models, developing novel machine learning techniques, training and evaluating large-scale ML models, collaborating with cross-functional teams, and mentoring junior researchers.
The position offers the opportunity to explore novel ideas, train large-scale models, and work closely with other researchers and engineers.
Representative projects include exploring new attention mechanisms in transformer architectures, investigating 3D animation synthesis methods, developing algorithms for on-device AI, and researching techniques for creating new AI voices.

Requirements:

A PhD or equivalent experience in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field is required.
A strong background in machine learning research, demonstrated by publications or significant contributions to the field, is essential.
Experience in training large models and working with state-of-the-art approaches in machine learning is necessary.
Proficiency with ML frameworks such as PyTorch, TensorFlow, or JAX is required.
Knowledge of advanced AI architectures, including transformers, diffusion models, and reinforcement learning techniques, is important.
Programming skills in Python or similar languages are required.
Familiarity with pre-training, fine-tuning, and evaluating LLMs such as LLaMA, Mistral, Qwen, etc., is a significant plus.

The US base salary range for this full-time position is $240,000 - $385,000, with total compensation including equity and benefits.
Individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs.
The base pay range is subject to change and may be modified in the future.