Machine Learning Engineer — AI Architecture Research at Featherless AI

Job description

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

Requirements

Must-haves

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
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
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

Nice-to-haves

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

Application prep

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Keywords to mirror

machine learningaiml

Platform: ashby
Effort: quick