Machine Learning Engineer — AI Architecture Research

About the Role We’re looking for a Machine Learning Engineer focused on AI architecture research to help design, prototype, and validate next-generation model architectures. You’ll work at the intersection of research and production — turning new ideas into scalable, real-world systems. This role is ideal for someone who enjoys questioning architectural assumptions, experimenting with novel model designs, and pushing beyond standard Transformer-style approaches. What You’ll Work On Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems) Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs) Prototype models end-to-end — from research code to training-ready implementations Collaborate with inference and systems engineers to ensure architectures are deployable and efficient Analyze model behavior, failure modes, and inductive biases Read, reproduce, and extend cutting-edge research papers Contribute to internal research notes, benchmarks, and open-source efforts (where applicable) What We’re Looking For Strong background in machine learning fundamentals and deep learning Hands-on experience implementing model architectures from scratch Solid understanding of: Attention mechanisms, RNNs, state-space models, or hybrid architectures Training dynamics, scaling behavior, and optimization Memory, latency, and compute constraints at the model level Comfortable working in PyTorch or JAX Ability to move fluidly between theory, experimentation, and engineering Clear communicator who can explain architectural trade-offs Nice to Have Experience with non-Transformer architectures (RNN variants, SSMs, long-context models) Background in research-driven startups or open-source ML projects Experience with large-scale training or custom training loops Publications, preprints, or notable research contributions Familiarity with inference optimization and deployment constraints Why Join Work on core model architecture, not just fine-tuning Direct influence on the technical direction of a Series-A company Small, high-caliber team with fast feedback loops Opportunity to ship research into production Competitive compensation + meaningful equity Originally posted on Himalayas