ML Engineer - Small Language Models

Introduction

Join us at Fastino as we build the next generation of LLMs. Our team, boasting alumni from Google Research, Apple, Stanford, and Cambridge is on a mission to develop specialized, efficient AI. Fastino's GLiNER family of open source models has been downloaded more than 5 million times and is used by companies such as NVIDIA, Meta, and Airbnb. Fastino has raised $25M (as featured in TechCrunch) through our seed round and is backed by leading investors including Microsoft, Khosla Ventures, Insight Partners, Github CEO Thomas Dohmke, Docker CEO Scott Johnston, and others.

What You’ll Work On

• Design, build, and deploy the critical small language models that are foundational to Fastino’s product.
• As an engineer on our team, you will own the full lifecycle of our state of the art models, from prototyping and data analysis to deployment, monitoring, and the continuous improvement of models in production.
• Drive the data strategy to continuously improve model performance by analyzing distribution gaps, contributing to synthetic data pipelines, and creating automated annotation systems.
• Experiment with novel language model architectures, helping drive and execute Fastino's research roadmap.
• 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 (Bachelors or Masters) in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline with concentrated study in deep learning or 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

• ML Engineering
• Small Language Models
• Deep Learning
• Reinforcement Learning
• Model Deployment
• Data Strategy
• LLM Architectures
• PyTorch
• JAX
• TensorFlow