Machine Learning Engineer — AI Architecture Research
Featherless AI8 months ago
Remote, WorldwideSenior
Responsibilities
- Research and develop new neural network architectures, including alternatives or extensions to Transformers, recurrent or hybrid models, and long-context systems.
- Design and run architecture-level experiments involving scaling laws, memory mechanisms, and compute trade-offs.
- Prototype models end-to-end from research code through training-ready implementations.
- Collaborate with inference and systems engineers to make architectures 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.
Requirements
- Strong background in machine learning fundamentals and deep learning.
- Hands-on experience implementing model architectures from scratch.
- Understanding of attention mechanisms, RNNs, state-space models, or hybrid architectures.
- Understanding of training dynamics, scaling behavior, and optimization.
- Understanding of memory, latency, and compute constraints at the model level.
- Comfort working with PyTorch or JAX.
- Ability to move between theory, experimentation, and engineering.
- Clear communication of architectural trade-offs.
- Experience with non-Transformer architectures, such as RNN variants, state-space models, or long-context models, is preferred.
- Background in research-driven startups or open-source machine learning projects is preferred.
- Experience with large-scale training or custom training loops is preferred.
- Publications, preprints, or notable research contributions are preferred.
- Familiarity with inference optimization and deployment constraints is preferred.
Benefits
- Opportunity to work on core model architecture rather than only 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.
- Meaningful equity is offered; compensation is described as competitive.
- Potential to contribute to open-source efforts where applicable.
Tech Stack
Categories
AI ResearchML Engineering
About Featherless AI
Featherless AI builds a serverless inference platform that orchestrates GPUs and load balances models so teams can deploy and scale open‑source AI without managing infrastructure. Its public cloud serves tens of thousands of open‑weight models and supports fine‑tuning, targeting developers, ML engineers, and enterprises needing reliable, high‑throughput inference. Founded in 2023 and headquartered in San Francisco, the privately held, Series A company is backed by investors including AMD and Airbus Ventures.