Top AI & Machine Learning Jobs in CO

Architect and deploy computer vision and perception systems for space-based missile defense. Develop C++ multi-band EO/IR image processing, physics-based feature extraction, object detection, tracking, and discrimination algorithms. Model infrared phenomenology, validate algorithms through high-fidelity software- and hardware-in-the-loop simulations, perform statistical performance analysis, and integrate reliable flight software for space-qualified processors.

Leads and grows a perception AI engineering team building autonomous spacecraft capabilities, including synthetic data generation, multi-target tracking, sensor fusion, camera calibration, object detection, and edge deployment. Provides technical direction, defines validation standards, owns the team roadmap, coordinates cross-functional delivery, manages hiring and development, and ensures reliable performance under spacecraft compute, power, and operational constraints.

Develop and evaluate machine learning models for object classification, anomaly detection, and data-driven space mission tasks. Support data ingestion, preprocessing, feature engineering, experimentation, and model evaluation. Write clean, tested Python code while collaborating with senior engineers. The role is fully onsite in Denver or Long Beach and requires compliance with U.S. ITAR technology export regulations.

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Build predictive failure models, real-time anomaly detection systems, diagnostic tools, and operational dashboards for spacecraft manufacturing, testing, and integration. Apply statistical modeling, causal inference, reliability analysis, and data mining to identify root causes, bottlenecks, quality issues, supplier risks, and process improvements. Develop reproducible Python and SQL analyses that support engineering decisions and mission reliability.

Build predictive models, anomaly detection systems, diagnostic tools, dashboards, and statistical quality-monitoring solutions for spacecraft manufacturing and testing. Analyze telemetry, production, supplier, and reliability data to identify failures, bottlenecks, delays, and root causes. Develop reproducible Python and SQL analyses and communicate actionable findings to engineering and operations stakeholders.

Leads and develops a team of autonomy engineers building multi-agent resource allocation, planning, coordination, and edge AI systems for spacecraft. Provides technical oversight on optimization, reinforcement learning, validation, architecture, and constrained real-time deployment. Owns team roadmaps, delivery, cross-functional dependencies, process improvement, and stakeholder communication while managing hiring, performance, career development, and team culture. The role bridges autonomy expertise with spacecraft constraints including compute, latency, power, and communication limitations.

Develop hybrid computer-vision and deep-learning perception systems for autonomous spacecraft. Responsibilities include multi-object tracking, neural-network detection and classification, coordinate transformations, image processing, synthetic-data generation, edge deployment, and software-, processor-, and hardware-in-the-loop validation. The role involves implementing Kalman filters, training PyTorch models, writing C++ flight code, and optimizing algorithms for space-qualified processors. This is a three-month temporary engagement with potential conversion to regular employment.

Analyze manufacturing and spacecraft telemetry to identify anomalies, test failures, and degradation patterns. Build predictive models, SQL queries, operational dashboards, statistical process controls, and visualizations using Python and related tools. Document reproducible analyses in Jupyter notebooks and communicate findings to engineering, manufacturing, and mission operations teams. This is a three-month temporary, potentially convertible role supporting hardware production and spacecraft operations.

Build and maintain AI platform infrastructure, connectors, API integrations, and custom AI-powered applications. Implement authentication, data pipelines, and tooling to embed LLM capabilities across teams. Contribute code, documentation, reviews, and enablement while ensuring security and compliance for government requirements.