Senior Machine Learning Engineer

YOUR MISSION

As a member of the Applied Algorithms and Autonomy team, you will design, build, and deploy core machine learning and AI capabilities for True Anomaly. You will work with a talented cross-functional team to advance technology at the intersection of artificial intelligence, machine learning, and data-driven decision-making. This will involve hands-on development across areas including object classification and discrimination, anomaly detection, and threat assessment. You are a first-principles engineer who takes ownership of the systems you build and delivers results.

• →Design, implement, and test ML/AI models that support threat assessment, object discrimination, and decision-making in operationally relevant environments
• →Own the full ML development lifecycle — from data ingestion and feature engineering through model training, evaluation, and production deployment
• →Collaborate with cross-functional teams to translate operational requirements into robust, production-ready ML capabilities
• →Establish and maintain rigorous model evaluation practices to ensure reliability and performance in real-world conditions
• →Write clean, well-documented, and testable code in support of AI/ML capabilities

• Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline
• Proficient in Python
• Solid understanding of statistics, probability, and optimization
• Experience with ML frameworks such as PyTorch, TensorFlow, or JAX
• 4+ years of experience designing, training, and deploying ML models in real-world systems
• Demonstrated ability to work in a multidisciplinary team and solve complex problems from first principles
• Passion for spaceflight and advancing capabilities related to space domain awareness and space security

• Master's or PhD in machine learning, computer science, data science, or a related discipline
• Strong background in one of the following core ML disciplines:
• Anomaly & outlier detection: statistical, density-based, and deep learning approaches
• Object discrimination: multi-class and fine-grained classification, metric learning, few-shot learning, evidential reasoning and Dempster-Shafer Theory (DST) for belief combination and conflict resolution under uncertain or incomplete sensor data
• Unsupervised learning: clustering, dimensionality reduction, generative modeling
• Sequential and temporal modeling: time-series analysis and sequential modeling
• Experience deploying models to edge or resource-constrained environments with real-time processing requirements
• Familiarity with space domain data such as space object catalog data, observational data, or RSO characterization
• Experience with MLOps tooling: experiment tracking (MLflow, W&B), model versioning, CI/CD for ML pipelines
• Background in model interpretability, uncertainty quantification, or safety-critical ML validation

• Work Location— this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily.
• Work environment—the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
• Physical demands—the physical demands of the job, including bending, sitting, lifting and driving.

This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite