Senior AI Engineer

Why Nimbus Data Systems ? At Nimbus Data Systems , we’re building the living map of activity in space. Through our proprietary global radar network and AI-enabled analytics platform, we collect millions of measurements daily on more than 25,000 objects in low Earth orbit (LEO). Our radar-powered intelligence protects billions in assets, monitors adversarial behavior, and ensures safe operations for commercial and government missions. We’re not just building technology, we are redefining global security, safety, and transparency in space. As orbital activity accelerates and threats grow more complex, Nimbus Data Systems is a trusted partner for Space Domain Awareness, Space Traffic Management, and Satellite Operations for top-tier space operators and allied defense organizations. If you're looking to work on mission-critical challenges at the forefront of aerospace, national security, and AI, your impact starts here. The Opportunity We are seeking an experienced and mission-driven Senior AI Engineer to join Nimbus Data Systems ’ growing Insights team. You will play a critical role in designing, building, and operating AI- and machine learning-powered systems that enable real-time space domain awareness and drive customer-facing insights. You will work at the intersection of machine learning, data engineering, and software engineering—developing scalable pipelines, deploying models into production, and integrating AI capabilities into operational systems. This includes transforming large-scale sensor and orbital datasets into intelligent systems that detect atterns, identify anomalies, and generate predictive insights. This role is highly hands-on and systems-oriented, with a focus on helping to define and drive Nimbus Data Systems ’ utilization of the latest Agentic AI technology. You will own the full lifecycle of AI solutions—from data and feature pipelines to model deployment, monitoring, and continuous improvement—while helping define best practices for applied AI across Nimbus Data Systems . Qualifications Must be eligible to obtain and maintain a U.S. personnel security clearance B.S. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Physics, or equivalent experience 5-7 years of experience in software engineering, machine learning engineering, or applied AI roles Up-to-date familiarity with the latest developments in Agentic AI Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) Advanced experience with SQL and large-scale data processing Proven experience developing and deploying production-grade machine learning models Experience working with large-scale distributed data platforms (e.g., Databricks, Spark) Strong understanding of statistical modeling, machine learning algorithms, and experimental design Experience designing and implementing feature engineering pipelines and training workflows Familiarity with MLOps practices, including model versioning, monitoring, and lifecycle management Strong problem-solving skills and ability to translate ambiguous real-world problems into scalable AI solutions Excellent communication skills, with the ability to influence technical and non-technical stakeholders Preferred Qualifications Experience building Agentic AI systems for time-series, anomaly detection, or predictive modeling Familiarity with Databricks ML, MLflow, or similar ML lifecycle platforms Experience deploying models into production systems with real-time or near-real-time constraints Background working with sensor, telemetry, or geospatial/orbital datasets Experience mentoring junior data scientists or leading technical initiatives Familiarity with streaming data systems (e.g., Kafka, Spark Structured Streaming) Background in orbital mechanics, aerospace, physics, or applied mathematics Active U.S. security clearance or ability to obtain one Within 1 Month, You’ll Complete onboarding to gain deep familiarity with Nimbus Data Systems ’ mission, products, and data ecosystem Set up development environments, data access, and ML tooling within the Databricks platform Review existing data pipelines, feature engineering workflows, and deployed models Begin contributing to model evaluation, analysis, or incremental improvements Within 3 Months, You’ll Develop a strong understanding of Nimbus Data Systems ’ data architecture, AI/ML use cases, and operational constraints Lead development of new models or enhancements to existing systems Design and implement feature pipelines and training datasets for key problem areas Partner with data engineering to improve data quality, scalability, and pipeline reliability Within 6 Months, You’ll Own end-to-end delivery of AI/ML solutions, from problem framing through production deployment Deploy and monitor models in production, ensuring reliability and performance Optimize model performance through advanced feature engineering, tuning, and experimentation Mentor junior team members and contribute to team best practices Within 12 Months, You’ll Act as a technical leader