Machine Learning Engineer (AI Architecture Research)

TL;DR

Machine Learning Engineer (AI Architecture Research): Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent/hybrid models, long-context systems) with an accent on architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs). Focus on prototyping models end-to-end from research code to training-ready implementations, analyzing model behavior and failure modes, and collaborating with inference engineers for deployable systems.

Location: Remote (world)

Company

Series-A AI company focused on next-generation model architectures.

What you will do

• Research and develop new neural network architectures including alternatives or extensions to Transformers, recurrent/hybrid models, and long-context systems
• Design and run architecture-level experiments on scaling laws, memory mechanisms, and 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 notes, benchmarks, and open-source efforts

Requirements

• 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
• Knowledge of training dynamics, scaling behavior, optimization, memory, latency, and compute constraints
• Comfortable working in PyTorch or JAX
• Ability to move fluidly between theory, experimentation, and engineering; clear communicator on 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

Culture & Benefits

• Work on core model architecture, not just fine-tuning
• Direct influence on 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