Machine Learning Engineer- Cloud Deployment (Remote)

True Anomaly seeks those with the talent and ambition to build the technology that secures it. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors - enabling the U.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. 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 ~ Experience with ML frameworks such as PyTorch, TensorFlow, or JAX ~4+ years of experience designing, training, and deploying ML models in real-world systems ~ 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 Sequential and temporal modeling: time-series analysis and sequential modeling 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 Equity + Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave Candidates must be based in or able to commute to our Denver or Long Beach office daily. To submit your application, please follow the directions below. #Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.If you have a disability or additional need that requires accommodation, please do not hesitate to let us.