Research Scientist - Small Language Models

Research Scientist - Small Language Models at Fastino Labs

Join Fastino Labs, a pioneering force in the next generation of Large Language Models (LLMs). With a team comprising alumni from Google Research, Apple, Stanford, and Cambridge, Fastino Labs is dedicated to developing specialized, efficient AI. Their GLiNER family of open-source models boasts over 5 million downloads, utilized by industry giants like NVIDIA, Meta, and Airbnb. Fastino Labs has secured $25M in seed funding, backed by prominent investors including Microsoft, Khosla Ventures, Insight Partners, Github CEO Thomas Dohmke, and Docker CEO Scott Johnston.

What You’ll Work On:

• Experiment with novel language model architectures, helping drive and execute Fastino's research roadmap
• Optimize Fastino’s multimodal models to improve response quality, instruction adherence, and overall performance metrics
• Architect data processing pipelines, implementing filtering, balancing, and captioning systems to ensure training data quality across diverse content categories
• Implement reinforcement learning techniques including Direct Preference Optimization and Generalized Reward Preference Optimization to align model outputs with human preferences and quality standards
• Build robust and real-world motivated evaluations
• Partner with Fastino engineering team to ship model updates directly to customers
• Establish best practices for code health and documentation on the team, to facilitate collaboration and reliable development

What We’re Looking For:

• Advanced degree (Master's or PhD) in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline with concentrated study in deep learning and computer vision methodologies
• Demonstrated ability to do independent research in Academic or Industry settings
• Substantial industry experience in large-scale deep learning model training, with demonstrated expertise in at least one of Large Language Models, Vision-Language Models, Diffusion Models, or comparable generative AI architectures
• Comprehensive technical proficiency and practical experience with leading deep learning frameworks, including advanced competency in one of PyTorch, JAX, TensorFlow, or equivalent platforms for model development and optimization

Key skills/competency

• Large Language Models (LLMs)
• Deep Learning
• Reinforcement Learning (DPO, GRPO)
• Multimodal Models
• Data Pipelines
• PyTorch
• JAX
• TensorFlow
• Generative AI
• Computer Vision