Research Scientist - Graph Foundation Models

Avra

2d•Hybrid

About The Position

In this role, you will define and execute an applied research agenda at the frontier of AI. You will build and optimize our entire ML pipeline — from data collection and preparation to training, evaluation, and production monitoring — while developing novel architectures that push the boundaries of what's possible. You'll fine-tune our foundational models for diverse client applications, and work on innovative solutions involving massive datasets and cutting-edge research. Additionally, you'll have the opportunity to work with global AI advisors who are leaders in research worldwide, and join a team that was selected by AWS as one of the three model builders in LATAM.

Requirements

At least 5 years in designing, implementing, and monitoring impactful machine learning models.
Bachelor’s degree (preferably with a specialization or Master’s) in Statistics, Computer Science, Mathematics, Physics, Economics, or a related quantitative field.
Deep, hands-on experience with PyTorch or similar deep learning frameworks.
Solid understanding of state-of-the-art modeling techniques.
Passion for frontier research, routinely exploring academic literature to drive innovation.
Track record of working with complex data pipelines and large-scale machine learning systems.
Experience in experimenting with and fine-tuning models using advanced hyperparameter tuning platforms.
Strong ability to design experiments, evaluate model performance, and implement algorithmic improvements based on the latest research.
Excellent communication skills and experience working in agile, cross-functional teams.
Ability to thrive in a dynamic, cross-functional team environment and comfortable taking full responsibility for your projects.

Nice To Haves

Experience with graph-based models and related technologies.
Prior experience with distributed systems and processing large volumes of data.
Active involvement in applied AI research, including publications or contributions to the research community.
Familiarity with MLOps practices for model deployment and production integration.

Responsibilities

Design, implement, and refine new machine learning and deep learning architectures by staying current with academic research and reading relevant papers daily.
Build, test, and monitor robust data pipelines and training processes, ensuring scalability and reliability in production environments.
Adapt and optimize our foundational models for various client needs, leveraging experimentation and hyperparameter tuning platforms.
Develop diverse AI-driven applications that integrate seamlessly with our platform, translating research insights into real-world solutions.
Collaborate with engineering and product teams to transform complex business challenges into technical solutions that harness the power of frontier research.
Take full technical ownership of research projects, making strategic decisions that directly influence the evolution of our products.