Machine Learning Engineer — AI Architecture Research

📋 Description

• Research and develop novel neural network architectures, including alternatives or extensions to
• Design and execute architecture-level experiments focused on scaling laws, memory mechanisms
• Prototype models end-to-end, translating research concepts into robust, training-ready
• Analyze model behavior, failure modes, inductive biases, and architectural strengths and
• Collaborate with inference and systems engineering teams to ensure new architectures are efficient
• Read, reproduce, evaluate, and extend cutting-edge machine learning research papers.

🎯 Requirements

• Strong foundation in machine learning and deep learning fundamentals, with practical experience
• Hands-on experience implementing neural network or model architectures from scratch.
• Strong understanding of attention mechanisms, RNNs, state-space models, hybrid architectures, or
• Solid knowledge of training dynamics, optimization, scaling behavior, and architecture-level
• Understanding of model-level memory, latency, compute, and efficiency constraints.
• Proficiency with PyTorch or JAX and the ability to develop and experiment with research-oriented ML

🎁 Benefits

• Competitive compensation and meaningful equity.
• Opportunity to work directly on core AI model architecture rather than focusing primarily on
• Significant influence over technical and research direction within a rapidly growing organization.
• Small, high-caliber team with fast feedback loops and a strong research-oriented environment.
• Opportunity to take research concepts from experimentation through to production deployment.
• Full-time position with a globally distributed work environment.