Research Scientist - Graph Foundation Models

About the role

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.

Responsibilities

• Innovative Model Architecture: Design, implement, and refine new machine learning and deep learning architectures by staying current with academic research and reading relevant papers daily.
• End-to-End Pipeline Management: Build, test, and monitor robust data pipelines and training processes, ensuring scalability and reliability in production environments.
• Model Fine-Tuning & Customization: Adapt and optimize our foundational models for various client needs, leveraging experimentation and hyperparameter tuning platforms.
• Application Development: Develop diverse AI-driven applications that integrate seamlessly with our platform, translating research insights into real-world solutions.
• Product Integration: Collaborate with engineering and product teams to transform complex business challenges into technical solutions that harness the power of frontier research.
• Technical Leadership: Take full technical ownership of research projects, making strategic decisions that directly influence the evolution of our products.
• You Stand Out If
• You have deep, hands-on experience with PyTorch or similar deep learning frameworks.
• You are passionate about frontier research, routinely exploring academic literature to drive innovation.
• You have a track record of working with complex data pipelines and large-scale machine learning systems.
• You're experienced in experimenting with and fine-tuning models using advanced hyperparameter tuning platforms.
• Experience with graph-based models is a plus.
• You thrive in a dynamic, cross-functional team environment and are comfortable taking full responsibility for your projects.

Requirements

• Experience: At least 5 years in designing, implementing, and monitoring impactful machine learning models.
• Academic Background: Bachelor's degree (preferably with a specialization or Master's) in Statistics, Computer Science, Mathematics, Physics, Economics, or a related quantitative field.
• Technical Proficiency: Extensive experience with modern deep learning frameworks (preferably PyTorch) and a solid understanding of state-of-the-art modeling techniques.
• Research Mindset: Strong ability to design experiments, evaluate model performance, and implement algorithmic improvements based on the latest research.
• Collaboration: Excellent communication skills and experience working in agile, cross-functional teams.
• Differentiators
• Prior experience with distributed systems and processing large volumes of data.
• Exposure to graph-based models and related technologies.
• Active involvement in applied AI research, including publications or contributions to the research community.
• Familiarity with MLOps practices for model deployment and production integration.

Benefits

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