Requirements

• Strong foundation in machine learning and deep learning fundamentals, with practical experience applying them to model development.
• Hands-on experience implementing neural network or model architectures from scratch.
• Strong understanding of attention mechanisms, RNNs, state-space models, hybrid architectures, or related approaches.
• Solid knowledge of training dynamics, optimization, scaling behavior, and architecture-level performance considerations.
• 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 code.
• Ability to evaluate architectural ideas through both theoretical reasoning and empirical experimentation.
• Strong communication skills, with the ability to clearly explain technical concepts and architectural trade-offs.
• Preferred experience with non-Transformer architectures such as RNN variants, state-space models, or long-context systems.
• Preferred background in research-driven startups, open-source machine learning projects, large-scale training, or custom training loops.
• Publications, preprints, notable research contributions, or experience with inference optimization and deployment constraints are advantageous.
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Benefits

• Competitive compensation and meaningful equity.
• Opportunity to work directly on core AI model architecture rather than focusing primarily on fine-tuning.
• 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.
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