Machine Learning Engineer — AI Architecture Research
At a glance
Mid-level Machine Learning Engineering role at Featherless AI. Remote · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Develop neural network designs, including Transformer alternatives, recurrent or hybrid models, and long-context systems
- Set up architecture experiments to compare scaling, memory approaches, and compute costs
- Build models from research code into implementations ready for training
- Work with inference and systems engineers to make architectures efficient and deployable
- Study model behavior and research papers, then share findings through notes, benchmarks, or open-source work
What you bring
- Strong grounding in machine learning and deep learning fundamentals
- Practical experience building model architectures from scratch
- Working knowledge of attention, RNNs, state-space or hybrid models, plus training dynamics and optimization
- Ability to use PyTorch or JAX and explain model architecture trade-offs clearly
Who this fits
This role suits a mid-level machine learning engineer who wants to move between architectural research, experiments, and implementation. It is remote and centers on model architecture work that can progress into production.
From the employer
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