Featherlessai
Machine Learning Engineer — AI Architecture Research
5mo ago
machine learningdeep learningneural networkstransformersrnn
Machine Learning Engineer focused on AI architecture research to design, prototype, and validate next-generation model architectures.
Responsibilities
- 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.
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
Conditions
- 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
Other
- 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