Machine Learning Engineer — AI Architecture Research

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.

• 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)

• 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

• 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

• 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