MACHINE LEARNING ENGINEER — AI ARCHITECTURE RESEARCH

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. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1