Machine Learning Systems Optimization Resident
adaption
Optimize machine learning systems across software and hardware while applying Python, deep learning, and full-stack systems thinking. As a Modelling Resident, you will pursue system-wide efficiency gains across software, hardware, and algorithmic domains. Your work connects model optimization with real-world product impact. Relevant projects may involve synthetic data, interfaces, real-time alignment, or algorithmic optimization. Key Responsibilities:
Drive efficiency gains across complete technical systems Collaborate across software, hardware, and algorithmic domains Develop algorithms reacting to real-time feedback Investigate new approaches to product signal capture Connect algorithm quality with real-world impact Requirements:
Computer science degree or equivalent engineering experience Systems thinking across the full machine learning stack Strong Python programming ability Experience with PyTorch, JAX, or TensorFlow Familiarity with RLHF and model fine-tuning Bring cross-stack optimization, Python, and deep learning framework experience to systems shaping efficient, continually adapting intelligence.
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Drive efficiency gains across complete technical systems Collaborate across software, hardware, and algorithmic domains Develop algorithms reacting to real-time feedback Investigate new approaches to product signal capture Connect algorithm quality with real-world impact Requirements:
Computer science degree or equivalent engineering experience Systems thinking across the full machine learning stack Strong Python programming ability Experience with PyTorch, JAX, or TensorFlow Familiarity with RLHF and model fine-tuning Bring cross-stack optimization, Python, and deep learning framework experience to systems shaping efficient, continually adapting intelligence.
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Reference: WJ-3875_16155811 · Posted 11 Nov 2026, 08:49 PM