Machine Learning Engineer — AI Architecture Research

The role

This person will research, prototype, and validate next-generation model architectures. You will work at the intersection of research and production to turn new ideas into scalable systems.

What you will do

• Research and develop new neural network architectures
• Design and run architecture-level experiments
• Prototype models end-to-end from research code to training-ready implementations
• Collaborate with inference and systems engineers
• 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

Required qualifications

• 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
• Solid understanding of training dynamics, scaling behavior, and optimization
• Solid understanding of 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

Benefits

• 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

About the company

featherlessai is a Series-A company with a small, high-caliber team.