Conducts foundational research on LLMs and multimodal systems, developing architectures, training methods, and optimization techniques and helping transition prototypes into production. The role requires an AI/ML research background, analytical problem-solving, programming experience, and strong research communication.
200k – 350k/yr
On-siteAI Research
About the role
Responsibilities
Conduct foundational research to advance the capabilities, efficiency, and reliability of LLMs 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 quickly to improve model quality.
Contribute to internal research strategy by identifying high-impact opportunities and emerging trends in AI.
Requirements
Research background in artificial intelligence, machine learning, physics, or a similar field.
Experience solving analytical problems using analytic and quantitative approaches.
Experience communicating research to audiences with different backgrounds.
Experience coding in C/C++, Python, or similar languages.
Nice-to-Haves
PhD in computer science, computational physics, mathematics, or a similar field.
Research and engineering experience demonstrated through grants, fellowships, patents, internships, work experience, and/or coding competitions.
First-author publications at peer-reviewed conferences or journals.
Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
Benefits
Work on challenging AI infrastructure problems, including low-latency inference and scalable model serving.
Build with cutting-edge technology affecting how businesses and developers use AI.
Significant ownership and impact in a fast-growing team.
Collaboration with experienced engineers and AI researchers.
Skills
artificial intelligenceMachine LearningLLMsmultimodal systemsc/c++PythonPyTorchTensorFlowJAXDeep LearningDistributed Systemsoptimizationmodel architecturesModel Trainingscalable model serving
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