Modelling Resident
8-month modelling residency embedded in production and research projects, focusing on adaptive algorithms, real-time learning, model efficiency, and cross-stack optimization. Requires Python, deep learning frameworks, and ML optimization experience.
About the job
Responsibilities
- Innovation: Build the future of adaptive algorithms that continuously learn. Co-design algorithms that react in real-time to product signal and feedback, and explore new ways of capturing feedback that make those algorithms better.
- Cross-Stack Optimization: Collaborate across software, hardware, and algorithmic domains to drive system-wide efficiency gains.
- Measure What Matters: Focus on real-world impact through product signal and interaction with the world.
Qualifications
- A degree or equivalent research/engineering experience in a computer science field.
- Genuine interest in, and at least one project touching: model efficiency, synthetic data, interfaces, real-time alignment, or algorithmic optimization.
- Systems thinking — the ability to understand and optimize across the full ML stack.
- Strong Python skills and experience with deep learning frameworks (PyTorch, JAX, or TensorFlow).
- Familiarity with model optimization techniques such as RLHF and fine-tuning.
- Strong communication and self-awareness — ability to collaborate in a remote environment and openness to feedback.
- Care about technical excellence and last mile impact; value impact more than effort, and own outcomes end to end.
Skills
Python, PyTorch, JAX, TensorFlow, RLHF, Fine-Tuning, Model Optimization, Deep Learning, Systems Thinking, Algorithmic Optimization
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