onsite
Senior Applied Scientist - Tabular Machine Learning
ML Engineer
Senior Applied Scientist specializing in tabular machine‑learning research and production. Lead benchmarking, debugging, and experimentation of deep‑learning models for structured data using Python, TensorFlow/PyTorch, and advanced statistical techniques.
About the role
Key Responsibilities
- Design, develop, and evaluate state‑of‑the‑art deep‑learning models for tabular data, translating research breakthroughs into production‑ready solutions.
- Build and maintain robust benchmarking pipelines to compare model performance, scalability, and resource utilization across diverse datasets.
- Debug complex training workflows, identify failure modes, and implement systematic troubleshooting strategies.
- Lead end‑to‑end experimentation cycles, including hypothesis formulation, data preprocessing, model training, and rigorous statistical analysis.
- Collaborate with cross‑functional teams to integrate tabular ML models into larger AI systems and ensure reproducibility.
Requirements
- Ph.D. or equivalent experience in Computer Science, Statistics, or a related field with a focus on machine learning.
- 5+ years of hands‑on experience developing deep‑learning models for structured/tabular data using Python and frameworks such as TensorFlow or PyTorch.
- Proven expertise in benchmarking, performance analysis, and debugging large‑scale ML experiments.
- Strong background in statistical modeling, feature engineering, and experimental design.
- Excellent problem‑solving skills and ability to communicate technical concepts to diverse audiences.
Skills
pythontensorflowpytorchmachine learningdeep learning