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Member of Technical Staff - Machine Learning / Foundation Model
EuropePosted 6 months ago
Full-timeremote
Job Description
Build the Relational Foundation Model powering the modern data stack.
Join us at the heart of the Kumo Relational Foundation Model architecture, where you'll play a pivotal role in shaping and expanding its capabilities. You'll drive innovative designs that enhance our Transformer and GNN-based models to operate seamlessly over any relational schema and heterogeneous graph. You’ll tackle high-impact use cases such as recommendation, forecasting, entity matching, customer retention, predictive maintenance, and fraud detection, all built on top of a single, extensible foundation model. Your work will span the full lifecycle of modern ML systems: from architecture design and training to post-training optimization and inference acceleration.
Leverage your expertise in machine learning and AI to solve critical challenges, creating scalable and adaptable solutions that push the boundaries of what’s possible. Get ready to make a significant impact in a dynamic and exciting environment!
Your Foundation:
- You want to solve real-world problems and get things done.
- Ph.D. in Computer Science or Machine Learning or related technical discipline, or related practical experience.
- 3+ years experience in designing ML algorithm solutions
- 2+ graph-based machine learning papers at top venues such as NeurIPS, ICLR, ICML, etc
- Well experienced in Python and its deep learning framework ecosystem, such as PyTorch
Your Extra Special Sauce:
- Passionate about OSS, including various contributions
- Experience developing large-scale systems
- Ability to diagnose technical problems, debug code, and automate routine tasks
- Analytical approach coupled with solid communication skills and a sense of ownership
Benefits
- Stock
- Competitive Salaries
- Medical, dental insurance
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.