Machine Learning Engineer Principal

SAIC is seeking an AI/Machine Learning Engineer to support the Enterprise Center of Excellence (ECOE) in developing scalable, data-driven solutions for mission-critical aerospace and defense systems.

This role focuses on building applied machine learning capabilities on top of structured, governed data systems, enabling predictive analytics, anomaly detection, and decision support at the aircraft and system level.

The position bridges data engineering, analytics, and machine learning, supporting the transition from file-based data processing to enterprise data warehouse-driven intelligence., Data Pipeline & ML Integration

• Develop and maintain end-to-end data pipelines (API ingestion, transformation, and loading into structured data stores)
• Integrate machine learning models into production data workflows
• Work with cloud-based data platforms (AWS, S3, RDS/PostgreSQL)

Machine Learning & Analytics

• Develop and evaluate machine learning models for prediction, classification, and anomaly detection.
• Apply statistical analysis and NLP techniques to structured and semi-structured datasets
• Support feature engineering aligned with enterprise data models

Data Quality & Validation

• Ensure data integrity, traceability, and validation across pipelines and models
• Automate data validation and testing processes using Python and SQL
• Support governed data frameworks and reproducible analytics

Cloud & Big Data Technologies

• Work with AWS services (S3, EC2, Athena) and/or Google Cloud (BigQuery)
• Utilize distributed data processing tools (e.g., Spark) where applicable
• Support scalable data architectures for analytics and ML workloads

Stakeholder Engagement

• Collaborate with engineers, data scientists, and domain experts
• Present analytical findings and model outputs to technical and non-technical stakeholders
• Support customer-facing discussions and solution development

• Bachelors and nine (9) years or more experience; Masters and seven (7) years or more experience; PhD or JD and four (4) years or more experience.
• Must be able to obtain a Secret Clearance after start
• Experience integrating and analyzing data from multiple sensor modalities and enterprise sources (e.g., maintenance, logistics, supply) to support readiness and maintenance analytics.
• Proficiency working in a Linux environment, including scripting, debugging, and system-level operations to support deployment and automation.
• Experience developing scalable data pipelines and orchestrating workflows (e.g., Airflow, NiFi, or similar tools).
• 3-7 years of experience in data engineering, analytics engineering, or applied ML
• Strong proficiency in:

• Python
• SQL
• Data pipeline development (API ingestion, ETL/ELT)

• Experience with cloud platforms (AWS or Google Cloud Platform)
• Experience working with structured data systems and relational databases
• Understanding of machine learning fundamentals and model evaluation

DESIRED SKILLS:

• Master's degree in Data Science or related field
• Experience with:

• Natural Language Processing (NLP)
• Spark or distributed data processing
• BigQuery or Snowflake

• Background in:

• Data validation, quality engineering, or regulated environments

• Experience presenting to stakeholders or customers
• Exposure to predictive modeling or forecasting use cases
• Strong data engineering mindset (pipelines, structure, automation)
• Ability to operationalize ML models-not just prototype
• Understanding of data governance and reproducibility
• Experience working in cross-functional engineering teams
• Strong communication and stakeholder engagement skills

Target salary range: $160,001 - $200,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.