Senior ML Engineer

Key Responsibilities

• Architect and maintain production-grade ML pipelines and infrastructure to support program-wide CBM+ initiatives.
• Oversee end-to-end data collection and processing, including the implementation and sustainment of robust ETL pipelines.
• Drive the adoption of MLOps practices to ensure automated model deployment, versioning, and rigorous monitoring.
• Partner closely with data scientists to transition models from experimental stages to production environments efficiently.
• Clean, preprocess, and manage large-scale datasets to ensure high-quality inputs for predictive modeling.
• Perform Exploratory Data Analysis (EDA) and statistical visualization to identify correlations and trends that drive product development.
• Identify and implement opportunities to improve system performance, scalability, and computational efficiency.
• Continuously monitor deployed models for performance drift and degradation, implementing retraining strategies as needed.
• Collaborate within a multi-functional Agile team to align technical tasks with evolving mission objectives.

Required Experience/Clearance

US citizen with a Secret US government clearance. Applicants who are not US Citizens and who do not have a current active Secret security clearance will not be considered for this role.
Bachelor’s degree in a STEM field (e.g., Computer Science, Data Science, Engineering, or Mathematics).
4 years of professional experience in machine learning or data science, with a focus on production-grade deployments.
Demonstrated experience with Databricks, PySpark, and SQL for large-scale data manipulation.
Proven experience with MLOps practices, including model engineering, orchestration, and monitoring.
Background in applied Natural Language Processing (NLP), including data labeling and entity/keyword extraction.
Strong understanding of statistical distributions and their application in predictive data modeling.
Excellent communication skills with the ability to present technical findings to diverse audiences and stakeholders.
Proficiency with office productivity software, including Microsoft Excel, Word, and PowerPoint.
Ability to travel, as needed, to engage with government customers and stakeholders.

Desired Experience

• Master’s degree in a STEM field, ideally with a focus on Data Science or Artificial Intelligence.
• Advanced understanding of AI/ML applications specifically within predictive maintenance or industrial logistics.
• Hands-on experience with sensor-based failure predictions and time-series modeling.
• Familiarity with Monte-Carlo simulations and the calculation of confidence levels for predictive forecasting.
• Experience working within DoD-specific data environments (e.g., Advana or Jupiter).
• Contributions to open-source ML projects or experience with Git-based collaboration.