Machine Learning Engineer

Responsibilities

• Research and develop new neural network architectures including alternatives to Transformers, recurrent/hybrid models, and long-context systems
• Design and run architecture-level experiments regarding 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 research 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, and optimization
• Understanding of memory, latency, and compute constraints at the model level
• Proficiency in PyTorch or JAX
• Ability to move fluidly between theory, experimentation, and engineering
• Clear communication skills regarding architectural trade-offs

Preferred Qualifications

• Experience with non-Transformer architectures such as RNN variants or SSMs
• 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

About the Company

This is a Series-A company offering the opportunity to work on core model architecture rather than just fine-tuning, within a small, high-caliber team.