Machine Learning Engineer

About the Role

As a Machine Learning Engineer at Aitium Inc, you'll own the full ML lifecycle — from data pipeline design through model training, evaluation, and production deployment with continuous monitoring. You'll build custom models for enterprise clients in healthcare analytics, financial forecasting, and operational optimization that deliver measurable business results.

This hybrid role is based out of Georgetown, TX with flexibility to work remotely several days per week. You'll collaborate closely with AI Engineers, data scientists, and client stakeholders.

What You'll Do

• Design and implement end-to-end ML pipelines from raw data ingestion through model serving
• Train, evaluate, and iterate on custom models using PyTorch and TensorFlow for structured and unstructured data
• Build robust feature engineering pipelines and maintain feature stores for enterprise datasets
• Deploy models to production using Kubernetes and cloud-native infrastructure on AWS and GCP
• Implement monitoring, drift detection, and automated retraining workflows to maintain model quality
• Work with clients to define evaluation metrics and success criteria for ML systems
• Document model architecture decisions, training procedures, and performance benchmarks

What We're Looking For

• 4+ years of experience building and deploying machine learning models in production
• Strong proficiency in Python, PyTorch, and/or TensorFlow
• Deep understanding of ML fundamentals — gradient descent, regularization, model evaluation, bias-variance tradeoff
• Experience with feature engineering for tabular, time-series, and text data
• Familiarity with containerization (Docker, Kubernetes) and cloud platforms (AWS SageMaker, GCP Vertex AI)
• Experience with experiment tracking tools (MLflow, Weights & Biases)
• Strong SQL skills and experience with large-scale data processing (Spark, BigQuery, Redshift)

Nice to Have

• Experience in healthcare ML (clinical NLP, biomarker modeling, genomics)
• Familiarity with causal inference and time-series forecasting
• Published research or open-source contributions in ML
• Experience with privacy-preserving ML (federated learning, differential privacy)

Tech Stack

What We Offer

• Competitive salary + performance bonus
• Hybrid schedule with remote flexibility
• $3,000 annual learning budget
• Full health, dental, and vision coverage
• 401(k) with company match
• Access to cutting-edge datasets and compute resources
• Mentorship from senior ML researchers and engineers