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AI/ML Engineer – Tabular Foundation Models
Ekimetrics. Review literature on latent reasoning in NLP and tabular foundation models .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates strong foundations in machine learning and statistics, with expertise in Python and PyTorch for implementing latent reasoning mechanisms in tabular foundation models. Capable of conducting thorough model validation and contributing to scientific publications through rigorous evaluation protocols.
Highest-signal resume keywords
Machine LearningPython ProgrammingPyTorch FrameworkModel ValidationTransformers Knowledge
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Latent ReasoningStatistical AnalysisData Leakage PreventionGradient BoostingRepresentation AnalysisProbing TechniquesActivation PatchingEvaluation ProtocolsTabular DatasetsScientific Documentation
Soft Skills
Analytical SkillsAutonomous Work
Tools & Technologies
PandasPolarsScikit-LearnGitCode ReviewTesting
Industry Keywords
NLPTabPFNTabICLTabDPTApplied AI Research
Tech Stack
Tools & technologiesPandasPythonPyTorchScikit-Learn
About the role
Key responsibilities & impact- Review literature on latent reasoning in NLP and tabular foundation models
- Identify and adapt latent reasoning mechanisms to tabular foundation model architectures
- Implement mechanisms on open-source tabular foundation models
- Measure effects on accuracy, calibration, and robustness against gradient boosting and standard tabular foundation model baselines at comparable compute
- Apply representation analysis, probing, and activation patching to characterize latent-reasoning computation
- Build evaluation protocols on diverse tabular datasets, including realistic business cases
- Document results and contribute to scientific publications
- Work with senior researchers and data scientists on applied AI research and production-grade engineering
Requirements
What you’ll need- Currently in the final year of a Master's or Engineering degree
- Strong foundations in machine learning, statistics, and programming
- Good grasp of model validation, including train/test splits, data leakage, and baselines
- Strong analytical skills and ability to work autonomously on an open-ended research problem
- Knowledge of transformers and language models
- Strong Python and PyTorch skills
- Solid command of pandas/polars, scikit-learn, and gradient-boosting libraries
- Fluency in English is a plus
- Experience in interpretability, knowledge of TabPFN, TabICL, or TabDPT, and software engineering practices such as git, code review, and testing are nice to have
Benefits
Comp & perks- Hands-on research and engineering experience building a pioneering agentic system on real data science workflows
- Collaborative learning with data scientists, AI experts, and PhDs
- Skill development in LLM agents, AI system evaluation, and Databricks production tooling (Unity Catalog, MLflow)
- Opportunity to transition into a full-time Data Scientist role in AI or a PhD program after successful completion
- Opportunity to co-author and publish scientific papers
- Networking with Ekimetrics' industrial and academic partners
- Exposure to ethical, governed, and sustainable AI practices