Job Description
You will be responsible for designing new approaches for fine tuning tabular foundation models and inference on tabular foundation models (including those with graph transformer architectures) with structured data, creating agentic systems for multi-faceted data analysis, developing advanced personalization and customization techniques, and integrating your solutions into intelligent applications for industry verticals. You will conduct in-depth research, produce production-ready scalable code, and collaborate with a cross-functional team to integrate your work into applications and services. You will be responsible for designing automated data quality assessment, automated data transformation for non-normal distributions and data asset search.
This role requires deep expertise in transformer models, machine learning, data science and generative AI, including large tabular foundation models (fine-tuning, adaptation, personalization), agents/agent frameworks, and tool orchestration. Preferred qualifications include practical experience with machine learning approaches for structured data, tabular representation learning, graph transformers, graph neural networks, time series modeling, and operations research/mathematical optimization.
Candidates should have the demonstrated ability to design novel solutions that advance the state of the art in response to business requirements, as evidenced by publications in first-tier conferences or journals and impact on successfully deployed software products that operate at scale.
Responsibilities
- Collaborate with a larger science team to develop innovative science solutions in response to business requirements.
- Research and develop innovative approaches leveraging recent advances in tabular foundation models, graph transformers, generative AI, mathematical optimization, and machine learning for structured data.
- Write high quality code to power experiments and build models. Contribute to writing production model code.
- Collaborate with data engineers to provide guidelines for data collection and annotation.
- Collaborate closely with cross-functional teams to deliver solutions into various applications and products.
- Identify new opportunities for scientific exploration and evaluate emerging technologies.
Qualifications and Experience:
- PhD in Computer Science, Mathematics, Statistics, Engineering, or a related field and a minimum of 5 years work experience in Deep Learning/Data Science/Machine Learning in an industry setting.
- Publication record in first-tier journals or conferences.
- Hands-on experience in training, fine tuning and large scale inference from foundation models, generative AI and model/application evaluation.
- Proficiency in Python, SQL, Spark.
- Strong understanding of transformer architecture and machine learning algorithms.
- Excellent problem-solving and analytical skills
- Strong communication abilities.
Qualifications
Career Level - IC3
About Us
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