Key Responsibilities:
- Design and develop elegant full-stack solutions that make advanced ML capabilities accessible to users without machine learning expertise
- Create intuitive frontend interfaces using modern JavaScript frameworks (React) that visualize complex data and model outputs
- Build robust backend APIs and microservices in Fast API that integrate with our relational foundation models
- Implement database schemas and workflows (experience with Temporal is a plus)
- Design clean architecture that enables AI-driven features like automated insights, workflow suggestions, and intelligent assistants
- Work closely with product, design, and ML teams to translate cutting-edge capabilities into delightful user experiences
- Write high-quality, tested code and participate in code reviews
Minimum qualification:
- BS (preferred MS, PhD) in Computer Science or related technical field involving coding, or equivalent technical experience
- 3+ years of industry experience as a software engineer
- Strong experience designing backend APIs, database schemas and microservices
- Proficiency with modern JavaScript and frameworks (React)
- Knowledge of current best practices in full-stack architecture, including performance, accessibility, security, and usability
- Experience with Test Driven Development
- Basic understanding of machine learning concepts and data workflows
Preferred Qualifications
- 5+ years of relevant experience as a SWE
- Past experience launching SaaS products or working in Enterprise companies
- Experience with vector databases, embeddings, or LLM-powered applications
- Familiarity with workflow orchestration tools (like Temporal)
- Understanding of relational data, SQL, and data transformation
- Experience building data visualization or interactive analytics tools
- Strong communication skills and ability to work effectively with leadership and cross-functional teams
- Highly data-driven approach to decision making
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What We Do
Democratizing AI on the Modern Data Stack!
The team behind PyG (PyG.org) is working on a turn-key solution for AI over large scale data warehouses. We believe the future of ML is a seamless integration between modern cloud data warehouses and AI algorithms. Our ML infrastructure massively simplifies the training and deployment of ML models on complex data.
With over 40,000 monthly downloads and nearly 13,000 Github stars, PyG is the ultimate platform for training and development of Graph Neural Network (GNN) architectures. GNNs -- one of the hottest areas of machine learning now -- are a class of deep learning models that generalize Transformer and CNN architectures and enable us to apply the power of deep learning to complex data. GNNs are unique in a sense that they can be applied to data of different shapes and modalities.
