Staff/Senior Research Scientist driving development of AI frontier benchmarks and datasets at Snorkel AI. Collaborating across teams to scale production and impact research community.
About the role
- Foundational AI Research Scientist developing next-generation language models. Pioneering large-language-model architectures and attention mechanisms for efficient scaling.
Responsibilities
- Research and prototype sub-quadratic attention architectures to unlock efficient scaling of large language models.
- Design and evaluate efficient attention mechanisms including state-space models (e.g., Mamba), linear attention variants, and sparse attention patterns.
- Lead pre-training initiatives across a range of model scales from 1B to 100B+ parameters.
- Conduct rigorous experiments measuring the efficiency, performance, and scaling characteristics of novel architectures.
- Collaborate closely with product and engineering teams to integrate models into production systems.
- Stay at the forefront of foundational research and help shape Aldea's long-term model roadmap.
Requirements
- Requires a Ph.D. in Computer Science, Engineering, or related field.
- 3+ years of relevant industry experience.
- Deep understanding of modern sequence modeling architectures including State Space Models (SSMs), Sparse Attention mechanisms, Mixture of Experts (MoE), and Linear Attention variants.
- Hands-on experience pre-training large language models across a range of scales (1B+ parameters).
- Expertise in PyTorch, Transformers, and large-scale deep-learning frameworks.
- Proven ability to design and evaluate complex research experiments.
- Demonstrated research impact through patents, deployed systems, or core-model contributions.
- Nice to Have Experience with distributed training frameworks and multi-node optimization.
- Knowledge of GPU acceleration, CUDA kernels, or Triton optimization.
- Publication record in top-tier ML venues (NeurIPS, ICML, ICLR) focused on architecture research.
- Experience with model scaling laws and efficiency-performance tradeoffs.
- Background in hybrid architectures combining attention with alternative sequence modeling approaches.
- Familiarity with training stability techniques for large-scale pre-training runs.
Benefits
- Competitive base salary
- Performance-based bonus aligned with research and model milestones
- Equity participation
- Flexible Paid Time Off
- Comprehensive health, dental, and vision coverage
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