Machine Learning Engineer – AI Architecture Research
Posted 66ds ago
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Job Description
Machine Learning Engineer focusing on AI architecture research and prototyping next-generation models. Collaborating with a small team to influence a Series-A company's technical direction.
Responsibilities:
- 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)
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
- 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
Benefits:
- Competitive compensation + meaningful equity

















