Machine Learning Engineer — AI Architecture Research

Role overview

Accountabilities

• Research novel neural network architectures and alternatives or extensions to Transformers.
• Design experiments covering scaling laws, memory mechanisms, training behavior, and compute-performance trade-offs.
• Prototype end-to-end models as robust, training-ready implementations.
• Analyze model behavior, failure modes, inductive biases, and architectural limitations.
• Collaborate with inference and systems engineers on efficient and deployable architectures.
• Reproduce, evaluate, and extend current machine learning research.
• Contribute to research notes, benchmarks, experiments, and applicable open-source initiatives.
• Combine theoretical investigation, rapid experimentation, and production-oriented engineering.

Requirements

• Strong machine learning and deep learning foundation with practical model development experience.
• Hands-on experience implementing neural network or model architectures from scratch.
• Understanding of attention mechanisms, recurrent neural networks, state-space models, hybrid architectures, or related approaches.
• Knowledge of training dynamics, optimization, scaling behavior, and architecture-level performance.
• Understanding of model memory, latency, compute, and efficiency constraints.
• Proficiency with PyTorch or JAX for research-oriented machine learning code.
• Ability to evaluate architecture ideas through theoretical and empirical methods.
• Strong communication skills for explaining technical concepts and architectural trade-offs.

Preferred qualifications

• Experience with non-Transformer architectures, including recurrent variants, state-space models, or long-context systems.
• Background in research-driven startups, open-source machine learning, large-scale training, or custom training loops.
• Publications, preprints, notable research contributions, or inference optimization experience are advantageous.

Benefits and work environment

• The position offers competitive compensation and meaningful equity.
• The role provides influence over AI architecture and research direction.
• The team is small, high-caliber, and research-oriented with fast feedback loops.
• The position is full-time in a globally distributed work environment.