Machine Learning Engineer — AI Architecture Research
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
Originally posted on Himalayas