Applied AI Scientist, Small Language Model and AI Training

San Francisco, United StatesOn-site

Responsibilities

Lead research and development of novel training methodologies and architectures for small and efficient language models. •
Design, implement, and evaluate model training experiments to improve performance, robustness, and generalization of language models. •
Collaborate closely with research scientists and engineers on scalable training pipelines and model deployment strategies. •
Develop techniques for model compression, fine-tuning, and domain adaptation to optimize models for real-world applications. •
Ensure AI safety, fairness, and alignment principles are integrated into model training processes and evaluated rigorously. •
Mentor and support cross-functional teams on applied machine learning methods and best practices. •
Evaluate and integrate new tools, frameworks, and datasets to accelerate AI training workflows. •

As an Applied Scientist specializing in Small Language Models and AI Training, you will lead research and development efforts focused on building efficient, high-performance language models tailored for practical applications.
You will work closely with research, engineering, and product teams to advance model training techniques, optimize architectures, and scale AI solutions.
Your work will directly contribute to AI systems that are safe, interpretable, and impactful across diverse usage scenarios. What You’ll Do •
experience in applied research or engineering roles focused on training language models, ideally small or efficient models. •
Strong programming skills in Python and familiarity with machine learning frameworks such as PyTorch, TensorFlow, or JAX . •
Deep understanding of language model architectures, training techniques, and optimization strategies. •
Experience with distributed training, data pipeline design, and scalable AI infrastructure. •
Passion for AI safety, interpretability, and delivering user-centered AI technology. •
experience working with large and small language models in production or research settings. •
Background in reinforcement learning, prompt engineering, or transfer learning techniques. •
Experience with developer tools, APIs, or frameworks related to AI model integration and delivery. •