Member of Technical Staff, Research
Conduct foundational research on LLMs and multimodal systems, designing architectures and training methods and helping move prototypes into production. The role targets PhD researchers graduating by December 2026 with strong machine-learning research and programming experience.
About the job
Responsibilities
- Conduct foundational research advancing the capabilities, efficiency, and reliability of large language models and multimodal systems.
- Design, implement, and evaluate novel model architectures, training methods, and optimization techniques.
- Collaborate with engineering teams to transition research prototypes into production-grade systems.
- Analyze empirical results, identify performance bottlenecks, and iterate to improve model quality.
- Bring ideas and emerging directions from doctoral research into the internal research agenda.
Requirements
- PhD completed within the last 6 months or expected by December 2026 in Computer Science, Machine Learning, Computational Physics, Mathematics, Electrical Engineering, or a related field.
- Research background in artificial intelligence, machine learning, physics, or a related area, demonstrated through a dissertation, publications, or research projects.
- Experience solving analytical problems using analytical and quantitative approaches.
- Experience communicating research to audiences with different backgrounds.
- Proficiency in Python, C/C++, or similar programming languages.
- Hands-on experience with PyTorch, JAX, or TensorFlow.
Nice-to-haves
- First-authored publications at top-tier peer-reviewed conferences or journals such as NeurIPS, ICML, ICLR, or MLSys.
- Industry research internships or open-source contributions to machine-learning systems, model training, or inference projects.
- Research or engineering experience demonstrated through grants, fellowships, patents, or coding competitions.
- Experience with large-scale model training, distributed systems, GPU kernel development, or inference optimization.
- Experience taking research code beyond the prototype stage.
Skills
Artificial Intelligence, Machine Learning, LLMs, Multimodal Systems, Python, C/C++, PyTorch, JAX, TensorFlow, Distributed Systems, Gpu Kernels, Inference Optimization, Model Training, Deep Learning, Optimization
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