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Culture & CareersInterpretable AI Research Engineer
Midigator
Software Engineering, Data Science
Alpharetta, GA, USA
Posted on Mar 29, 2026
What you will do
- Develop methods to map latent embeddings to human concepts, with the explicit goal of labeling embedding dimensions/features as interpretable text descriptions relevant to credit risk modeling.
- Design and execute mechanistic interpretability research on Transformer architectures (e.g., probing representations, causal interventions, and internal component analysis) to understand how credit risk signals are encoded.
- Build an end-to-end interpretability workflow: experimental design, implementation, evaluation, and clear documentation of findings and limitations.
- Engineer research code into durable tooling: refactor experimental notebooks/prototypes into clean, modular, testable Python code that supports iteration and reuse.
- Collaborate with credit risk domain experts to ensure interpretability outputs are meaningful, actionable, and grounded in domain reality.
- Partner with internal MLOps / ML engineers to run large-scale training and integrate research tooling with GPU/cloud execution environments—while keeping momentum when support is intermittent.
- Contribute to technical strategy: propose experiments, define success criteria, and help de-risk the approach through principled iteration and evidence.
- Education & Experience: A PhD in a quantitative discipline is highly preferred (with a minimum of 5+ years of relevant professional/research experience) OR an MS with 7+ years of exceptional, specialized eXplainable AI (XAI) experience is acceptable.
- A strong, demonstrable background in interpretability, representation analysis, or research/tooling focused on Transformer models.
- Strong mathematical and statistical foundations (linear algebra, statistics) sufficient to implement/interpret representation analysis methods (the work references advanced techniques such as Kernel CCA).
- Experience with LLMs and Transformer development workflows (e.g., common training/evaluation patterns).
- Communication & documentation strength: ability to clearly explain complex findings to technical stakeholders and produce crisp experimental notes and interpretability artifacts.
- Strong research engineering skills in Python with demonstrated ability to implement and iterate quickly while maintaining code quality and reproducibility.
- Expertise with modern deep learning frameworks (TensorFlow strongly preferred / PyTorch acceptable)
- Hands-on experience in Mechanistic Interpretability / XAI for Transformers, including the ability to run controlled experiments that isolate and explain internal model behavior (e.g., activation-based interventions and interpretability analysis patterns).
- ML engineering experience running Transformers on cloud GPU infrastructure, including practical understanding of training large models with NVIDIA GPUs and troubleshooting training/runtime issues.
- Familiarity with Hugging Face transformers/datasets ecosystems and applied workflows for training/fine-tuning and data iteration.
- Exposure to high-volume data handling for time-series or sequential modeling (e.g., efficient data loading strategies; pipeline integration patterns), especially when datasets are large and irregular.
- Experience applying interpretability techniques to regulated or high-stakes domains (finance, healthcare, compliance-heavy environments), where explanation quality and defensibility matter.
- Evidence of impact via publications, open-source contributions, or internal tooling related to interpretability, representation learning, or Transformer analysis.
- You’ll tackle a high-novelty interpretability problem: translating Transformer embedding spaces into human concepts for real-world credit risk time-series data.
- You’ll have access to domain experts for credit risk meaning and labeling support—so your focus stays on the hardest technical bottleneck: mechanistic interpretability.