Medical Engineer

━ About the Role
We are looking for an ML Engineer to own model development on a federal data and AI program: self-supervised representation learning over tabular and temporal administrative data, language-model-based information extraction from unstructured records, supervised classification on learned embeddings, and model explainability. The work is performed against large linked datasets inside an access-restricted analytic environment with approved tooling, no external connectivity, and CPU-only compute.
This is a modeling role, not a pipeline role. You will be accountable for architecture selection, training, calibration and validation of the models the program depends on, and for the quantitative judgment behind each of those decisions. Data engineering is part of the job; it is not the center of it.
━ What You Will Do
Representation Learning
Design and train self-supervised tabular transformers (SAINT, TabTransformer, FT-Transformer) and temporal sequence models over longitudinal event data, with the outcome label withheld during pretraining.
Select pretraining objectives, execute pilot runs, and validate embedding quality before committing to full-scale training.
Benchmark candidate architectures against gradient-boosted baselines and document comparative results.

Language Model Extraction
Deploy locally hosted open-weight language models for structured information extraction from unstructured text, including prompt design, constrained output schemas and post-processing.
Quantize and tune models for CPU-only inference at volume without unacceptable degradation in extraction accuracy.
Implement the dual-coding evaluation protocol — stratified sampling, independent human coding, field-level agreement measurement — and report accuracy before volume extraction.

Supervised Modeling and Calibration
Train gradient-boosted classifiers (LightGBM, XGBoost) on learned embeddings under severe class imbalance.
Perform threshold calibration on the validation partition with the cost of each error type made explicit, and report calibration curves alongside discrimination metrics.
Detect and prevent leakage across temporal splits and across linked record sources.

Explainability
Generate additive feature attributions (SHAP) at the individual-prediction level and aggregate them across cohorts and subgroups.
Verify that attributions are stable across model retrains and consistent with domain expectations.

Supporting Data Work
~ Maintain the feature and inference pipelines the models depend on (Spark, Parquet, Python), and version every dataset, model and experiment in MLflow so reported results regenerate.

━ Mathematical Foundation
This is a modeling position and we weight mathematical grounding heavily. Applied fluency is expected in the following; the interview will test it.
Area
Expected depth

Probability and statistical inference: Estimators, bias and variance, sampling distributions; correct interpretation of confidence intervals and calibration curves
Linear algebra and optimization: Gradient-based optimization, regularization, convexity and its failure in deep networks; attention understood as matrix operations
Information theory: Cross-entropy, KL divergence, and the loss functions behind self-supervised and contrastive objectives
Evaluation theory: Discrimination versus calibration; ROC and precision-recall behavior under class imbalance; threshold selection by expected cost
Causal reasoning: Confounding and leakage; recognizing when a feature encodes the outcome or a downstream consequence of it

━ Technical Stack
Layer
Tools

Tabular representation learning: SAINT, TabTransformer, FT-Transformer; self-supervised pretraining objectives (masked-feature reconstruction, contrastive)
Temporal and sequence modeling: Transformer encoders over ordered event sequences (BEHRT-style), temporal convolutional networks
Language models: Open-weight instruction-tuned models (Llama, Mistral, Qwen families) hosted locally; quantized CPU inference via llama.cpp or equivalent
Supervised learning: LightGBM, XGBoost, CatBoost; scikit-learn
Deep learning framework: PyTorch
Explainability: SHAP (TreeSHAP, KernelSHAP), integrated gradients
Data and MLOps: Spark, Parquet, pandas, NumPy; MLflow for experiment tracking and model versioning
Environment: Cloud-hosted, access-restricted analytic environments

━ What Were Looking For
~3+ years training and deploying machine learning models, with end-to-end ownership of at least one system from raw data to validated result.
~ Hands-on experience training transformer architectures in PyTorch, including at least one self-supervised or contrastive objective on non-text data.
~ Experience deploying open-weight language models for structured extraction, with accuracy measured against a labeled reference set.
~ Demonstrated competence in calibration, class imbalance and leakage prevention on real