Machine Learning Engineer

The Judge Group; Westminster (CO); On-site; USD 90,000 - 140,000; Full time

Job summary

The Judge Group is seeking a Data Science Engineer to build and support advanced machine learning capabilities for Space Domain Awareness analytics systems. You will work on trajectory classification, anomaly detection, and scalable pipelines with a focus on sparse, noisy data.

The role requires deep ML and software engineering skills, collaboration with astrodynamics and embedded teams, and the ability to deliver operationally deployable models with attention to accuracy and performance.

Qualifications

• 4+ years of experience developing and deploying ML solutions.
• Strong Python and NumPy/SciPy/Pandas proficiency.
• Experience with PyTorch and modern DL frameworks.
• ML systems for time-series/sequential/tracking/state-estimation data.
• Ability to obtain and maintain a U.S. Security Clearance.
• Excellent technical writing and communication skills.
• Understanding of uncertainty quantification and bias analysis.

Responsibilities

• Develop AI/ML models for trajectory classification across orbital regimes and object families.
• Build anomaly detection models for unusual dynamical behavior and patterns.
• Design and maintain end-to-end analytical pipelines for observation processing and orbit estimation.
• Develop benchmarking and evaluation frameworks for model accuracy and calibration.
• Design experiments to validate generalization with held-out data and degradation studies.
• Create confidence/uncertainty metrics for operational decisions.
• Partner with embedded systems engineers to optimize model size and latency.
• Implement model optimization techniques (quantization, pruning).
• Contribute to data flow design, model lifecycle management, and roadmaps.
• Maintain a high-quality, reproducible codebase.

Skills

Python

NumPy

SciPy

Pandas

PyTorch

Time-series

Tracking

State-estimation

Uncertainty quantification

Model evaluation

Software engineering

Version control

Testing frameworks

Reproducible environments

Technical writing

Communication skills

U.S. Security clearance

Education

Bachelor's or Master's in CS/DS/Applied Math/Statistics/Aerospace Eng/Physics

Tools

Git

Docker

Jupyter

Job description

Compensation: Competitive (TBD)

Security Clearance: Must be eligible to obtain and maintain a U.S. Security Clearance

About the Role

We are seeking a highly skilled Data Science Engineer to build and support advanced machine learning capabilities for Space Domain Awareness (SDA) analytics systems. This role focuses on developing trajectory classification and anomaly detection models, maintaining scalable analytics pipelines, and creating robust evaluation frameworks for sparse, noisy, and incomplete observational data.

The ideal candidate combines strong machine learning expertise with software engineering discipline and has experience working with time-series, state-estimation, or tracking-related systems. You will collaborate closely with astrodynamics, software, and embedded systems teams to ensure models are both scientifically sound and operationally deployable.

What You'll Do

• Develop AI/ML models for trajectory classification across orbital regimes and object families.
• Build anomaly detection models to identify unusual dynamical behavior and emerging patterns.
• Design, integrate, and maintain end-to-end analytical pipelines covering observation processing, orbit estimation, propagation, hypothesis generation, and classification.
• Develop benchmarking and evaluation frameworks to assess model accuracy, convergence behavior, false-positive and false-negative rates, confidence calibration, and performance under degraded observation conditions.
• Design experiments that validate model generalization through held-out datasets, data degradation studies, and independent benchmark comparisons.
• Create calibrated confidence and uncertainty metrics suitable for operational decision-making.
• Partner with embedded systems engineers to optimize model size, memory usage, and inference latency for resource-constrained environments.
• Implement model optimization techniques such as quantization, pruning, and architecture simplification.
• Contribute to systems engineering activities, including data flow design, model lifecycle management, retraining strategies, and technology roadmap planning.
• Maintain a high-quality, reproducible, and well-documented codebase using software engineering best practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Applied Mathematics, Statistics, Aerospace Engineering, Physics, or a related quantitative discipline.
• 4+ years of experience developing and deploying machine learning solutions.
• Strong proficiency in Python and scientific computing libraries such as NumPy, SciPy, and Pandas.
• Experience with modern deep learning frameworks, preferably PyTorch.
• Proven experience building ML systems for time-series, sequential, tracking, or state-estimation-related data.
• Strong understanding of model evaluation methodologies, including:
• Uncertainty quantification
• Dataset bias and failure analysis
• Experience with version control, testing frameworks, reproducible environments, and software development best practices.
• Excellent technical writing and communication skills.
• Ability to obtain and maintain a U.S. Security Clearance.

Preferred Qualifications

• Experience with physics-informed machine learning or hybrid physics/ML approaches.
• Knowledge of orbit determination, object tracking, or multi-target data association methods (e.g., JPDA, MHT).
• Experience deploying machine learning models to embedded or edge computing platforms.
• Familiarity with model compression, quantization, and inference optimization techniques.
• Experience supporting government, defense, aerospace, or advanced R&D programs.
• Understanding of Technology Readiness Levels (TRLs) and product maturation processes.
• Experience developing anomaly detection systems for rare-event or weakly labeled datasets.

Why Join Us?

• Work on challenging, mission-critical problems in the space and advanced analytics domain.
• Collaborate with exceptional engineers, scientists, and technical experts.
• Develop cutting-edge machine learning solutions with real-world impact.
• Thrive in an innovative, fast-paced, and highly collaborative environment.
• Enjoy competitive compensation, benefits, and long-term growth opportunities.