Machine Learning Engineer

About the role

Status: Direct Hire

Position: Machine Learning Engineer

Location: Hybrid in Albuquerque, New Mexico

Salary: $165,000 - $190,000

Current active Top Secret Clearance

WHO WE ARE

We are a rapidly growing data science and artificial intelligence company on a mission to use data for good. We believe that with better information and the right tools, we can discover creative solutions for the most complex challenges facing our world today. By blending advanced computational capabilities, engineering, and user experience design, we build intuitive platforms that empower organizations to make critical decisions with confidence.

Our work addresses the most pressing issues of our time. We partner with government agencies, healthcare organizations, and innovative enterprises to tackle massive problems in disaster preparedness, national security, urban planning, and climate resilience. Whether we are optimizing critical infrastructure or using predictive modeling to improve public health, our ultimate goal is always to leave the planet safer and more resilient than we found it.

Machine Learning Engineer:

Our client is seeking a Machine Learning Engineer to lead the development, deployment, and ongoing optimization of production machine learning models and supporting MLOps pipelines. This role is ideal for an engineer who combines a strong foundation in machine learning, data science, and applied statistics with the systems expertise needed to transform complex, real-world data into reliable production capabilities. The ideal candidate will be comfortable owning the full ML lifecycle while collaborating closely with software, infrastructure, and technical program teams.

This role is a hybrid model in Albuquerque, New Mexico.

Machine Learning Engineer Responsibilities:

Own the end-to-end development and refinement of machine learning models, including supervised and unsupervised approaches for anomaly detection and operational monitoring.

Analyze complex, multivariate datasets, perform data profiling and feature engineering, evaluate modeling approaches, and continuously improve model performance against operational requirements.

Manage the complete MLOps lifecycle, including data ingestion, preprocessing, feature development, training, validation, deployment, model serving, and ongoing maintenance.

Apply sound statistical and mathematical methods to model selection, validation, uncertainty assessment, and performance measurement.

Develop and maintain scalable data preprocessing, enrichment, and feature pipelines that support current machine learning applications and future AI capabilities.

Establish monitoring strategies for model performance, drift, anomaly detection effectiveness, data quality, and production output.

Partner with software architecture and engineering teams to integrate machine learning models and outputs into broader applications and operational workflows.

Maintain standards for data quality, freshness, lineage, governance, and interfaces between machine learning pipelines and downstream applications.

Evaluate emerging machine learning technologies, foundation model approaches, and tooling to identify opportunities to enhance system capabilities.

Optimize data structures, processing methods, and retrieval patterns to support efficient inference and timely delivery of model outputs.

Produce clear technical documentation covering model architectures, assumptions, validation methods, pipeline designs, performance characteristics, and machine learning best practices.

Collaborate with technical and business stakeholders to translate operational needs into effective machine learning solutions and communicate modeling decisions, risks, and trade-offs.

Machine Learning Engineer Qualifications:

Active Secret or TS/SCI security clearance.

Bachelor's degree or equivalent professional experience in Computer Science, Statistics, Applied Mathematics, Data Science, or another quantitative or technical discipline.

Strong knowledge of machine learning, data science, applied statistics, and the mathematical principles underlying common modeling techniques.

Hands-on experience building, deploying, and maintaining production machine learning pipelines spanning preprocessing, feature engineering, training, evaluation, and model serving.

Proficiency with Python and machine learning frameworks or libraries such as scikit-learn, PyTorch, TensorFlow, or comparable technologies.

Demonstrated experience working with complex, real-world datasets requiring substantial cleaning, transformation, analysis, and feature engineering.

Ability to select appropriate statistical and machine learning methods, explain the reasoning behind technical decisions, and rigorously evaluate model performance.

Strong written and verbal communication skills, including the ability to communicate technical concepts, modeling decisions, and trade-offs to both technical and non-technical audiences.

Preferred:

Experience working with multivariate time-series data, anomaly detection, or machine learning applications operating in real-time or near-real-time environments.

Familiarity with telemetry, aerospace, sensor, or other complex operational data environments.

Experience deploying or supporting machine learning solutions within classified, air-gapped, restricted-access, or similarly controlled environments.

Experience with AWS machine learning and data services, such as SageMaker and Step Functions, or comparable cloud technologies.

Familiarity with MLOps platforms and tools such as MLflow, Kubeflow, or similar technologies.

AWS Machine Learning, Data Engineering, or related technical certifications.

Experience working in consulting, professional services, or other environments supporting multiple stakeholders or customers.