ML Engineer - Work From Home

About the Company

iAdeptive Technologies is an 8(a) small business delivering data, cloud, and AI engineering solutions for federal mission programs. The company focuses on building secure, scalable, and auditable systems, with engineers involved directly in designing and implementing technical solutions. Its teams work across architecture, engineering, security, and governance to deliver systems that meet demanding operational requirements.

About the Role

iAdeptive Technologies is seeking a Machine Learning Engineer to lead model development for a federal data and AI program. The role focuses on self-supervised representation learning, temporal modeling, language-model-based information extraction, supervised classification, model calibration, and explainability.

You'll take ownership of the modeling lifecycle, from architecture selection and experimentation to training, validation, and reproducible results. The work involves large, linked administrative datasets in a restricted analytical environment with approved tools, no external connectivity, and CPU-only computing.

This is a modeling-focused position rather than a data pipeline role. Strong mathematical reasoning and the ability to evaluate models critically are essential.

Key Responsibilities

• •Design and train self-supervised tabular transformers, including SAINT, TabTransformer, and FT-Transformer, along with temporal sequence models for longitudinal event data.
• •Select pretraining objectives, run experiments, evaluate embedding quality, and benchmark architectures against gradient-boosted baselines.
• •Deploy locally hosted, open-weight language models to extract structured information from unstructured records using prompt design, constrained output schemas, and post-processing.
• •Optimize language models for high-volume CPU inference through quantization and performance tuning while maintaining acceptable extraction accuracy.
• •Implement dual-coding evaluation protocols using stratified samples, independent human coding, and field-level agreement measurements before large-scale extraction.
• •Train gradient-boosted classifiers using LightGBM, XGBoost, or CatBoost on learned embeddings, accounting for severe class imbalance.
• •Calibrate classification thresholds based on the expected cost of different error types and report calibration alongside discrimination metrics.
• •Identify and prevent data leakage across temporal splits and linked record sources.
• •Generate SHAP-based feature attributions at the individual-prediction and cohort levels, assess subgroup patterns, and verify attribution stability across model retraining.
• •Maintain supporting feature and inference workflows using Python, Spark, and Parquet.
• •Use MLflow to track experiments and version datasets and models so reported results can be reproduced.
• •Document model architecture decisions, experimental findings, validation results, and the reasoning behind technical choices.

Required Qualifications

• •At least 3 years of experience training and deploying machine learning models, including end-to-end ownership of a system from raw data through validated results.
• •Hands-on experience training transformer architectures in PyTorch, including at least one self-supervised or contrastive learning objective on non-text data.
• •Experience deploying open-weight language models for structured information extraction and measuring accuracy against a labeled reference dataset.
• •Demonstrated ability to handle model calibration, class imbalance, and leakage prevention using real-world observational data.
• •Experience with gradient-boosted learning methods such as LightGBM, XGBoost, or CatBoost and SHAP-based explainability.
• •Advanced Python skills with NumPy, pandas, scikit-learn, and PyTorch.
• •Strong SQL skills and experience using Spark for large-scale feature engineering.
• •Applied mathematical knowledge covering probability, statistical inference, linear algebra, optimization, information theory, evaluation metrics, and causal reasoning.
• •Ability to work within access-restricted environments using approved tools and CPU-only computing.
• •U.S. work authorization and the ability to obtain a Public Trust or higher background determination.

Preferred Qualifications

• •Master's degree in mathematics, statistics, or applied mathematics with a concentration in data science. Computer science or another quantitative degree is also considered when supported by equivalent mathematical depth in academic coursework and professional experience.
• •Published research or open-source contributions involving tabular deep learning, event-sequence modeling, or LLM evaluation.
• •Experience with model quantization and large-scale CPU inference optimization.
• •Familiarity with healthcare claims or administrative datasets, including diagnosis and procedure coding systems.
• •Experience with fairness assessment and subgroup evaluation.
• •Experience working in FedRAMP, HIPAA, or other access-restricted environments.
• •Active or previous federal Public Trust determination.

Technical Stack

• •Representation Learning: SAINT, TabTransformer, FT-Transformer, masked-feature reconstruction, and contrastive learning.
• •Temporal Modeling: Transformer encoders for ordered event sequences, including BEHRT-style approaches, and temporal convolutional networks.
• •Language Models: Locally hosted Llama, Mistral, and Qwen model families; llama.cpp or equivalent CPU inference tools.
• •Supervised Learning: LightGBM, XGBoost, CatBoost, and scikit-learn.
• •Deep Learning: PyTorch.
• •Explainability: SHAP, TreeSHAP, KernelSHAP, and integrated gradients.
• •Data and MLOps: Python, Spark, Parquet, pandas, NumPy, and MLflow.
• •Environment: Cloud-hosted, access-restricted analytical environments.

Compensation and Benefits

• •Annual salary range of $90,000–$120,000.
• •Health, dental, and vision coverage.
• •401(k) with company contribution.
• •Paid time off and federal holidays.
• •Training and certification support.

The posted salary range represents the floor and target for the role. Actual offers depend on experience, certifications, and clearance level.

Equal Employment Opportunity

iAdeptive Technologies is an Equal Opportunity Employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.