Machine Learning Engineer — AI Architecture Research

Role overview

Accountabilities

• Research and develop novel neural network architectures, including alternatives or extensions to Transformers, recurrent models, hybrid models, and long-context systems.
• Design architecture-level experiments focused on scaling laws, memory mechanisms, training behavior, and compute-performance trade-offs.
• Prototype models end-to-end as robust, training-ready implementations.
• Analyze model behavior, failure modes, inductive biases, and architectural strengths and limitations.
• Collaborate with inference and systems engineering teams on efficient, scalable, deployable architectures.
• Read, reproduce, evaluate, and extend cutting-edge machine learning research papers.
• Contribute to research notes, benchmarks, experiments, and applicable open-source initiatives.
• Move between theoretical investigation, rapid experimentation, and production-oriented engineering.

Requirements

• Strong machine learning and deep learning fundamentals with practical model development experience.
• 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.
• Knowledge of training dynamics, optimization, scaling behavior, and architecture-level performance considerations.
• Understanding of model memory, latency, compute, and efficiency constraints.
• Proficiency with PyTorch or JAX and research-oriented machine learning code.
• Ability to evaluate architecture ideas through theoretical reasoning and empirical experimentation.
• Strong communication skills for explaining technical concepts and architectural trade-offs.
• Experience with non-Transformer architectures, research-driven startups, open-source projects, large-scale training, or custom training loops is preferred.
• Publications, preprints, notable research contributions, or inference optimization and deployment experience are advantageous.

Benefits and work environment

• Competitive compensation and meaningful equity are offered.
• The role focuses on core AI model architecture rather than primarily on fine-tuning.
• The position offers influence over technical and research direction within a rapidly growing organization.
• The team is small, high-caliber, research-oriented, and designed for fast feedback loops.
• The position is full-time and operates in a globally distributed work environment.