Machine Learning Engineer — AI Architecture Research

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer — AI Architecture Research based in Bulgaria.

This role focuses on researching and building next-generation AI model architectures that can move from experimental concepts to scalable production <systems.You> will work at the intersection of machine learning research, model engineering, and real-world deployment.The position offers the opportunity to challenge established architectural assumptions and explore alternatives to conventional Transformer-based <designs.You> will design experiments, prototype new neural networks, and evaluate trade-offs across compute, memory, latency, and model performance.The role involves close collaboration with inference and systems engineers to make research ideas efficient and <deployable.You> will also contribute to research reproduction, benchmarking, technical exploration, and potentially open-source work.This is an opportunity to have meaningful influence on AI architecture while working in a fast-moving, research-oriented environment.

Accountabilities

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

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.

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.