Top Computer Vision Jobs in Los Angeles, CA

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.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Develop, train, and optimize computer vision and ML models (particularly video-based safety) using petabyte-scale camera and sensor data. Deploy and optimize models for backend and edge inference, collaborate with cross-functional teams, and translate customer needs into scalable ML/CV solutions.

Lead the perception stack for unmanned aircraft, owning architecture, roadmap, team leadership, and hands-on delivery. Develop detection, recognition, and image-space tracking for difficult operational conditions, transition classical computer vision to learned models, define evaluation and performance metrics, and deploy optimized perception software to constrained embedded hardware. Collaborate with state estimation and guidance teams while ensuring latency, reliability, and deterministic fallback behavior.

Develop, validate, optimize, and deploy EO/IR image-processing and computer vision algorithms for space-based sensor payloads. Build image-system modeling and simulation frameworks, synthesize imagery for machine learning and validation, detect and track low-SNR objects, and transition algorithms to real-time software and firmware implementations. Analyze datasets, troubleshoot deployed systems, document performance, support technical milestones, and present results to government and technical stakeholders.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Develops computer vision and machine learning perception algorithms for autonomous space-threat detection. Responsibilities include training, evaluating, optimizing, and deploying models; predicting relative pose of dynamic targets; integrating perception into simulations and real-time autonomy pipelines; creating sensor models and digital twins; and maintaining model-training and evaluation infrastructure.

Develops advanced computer vision and image-processing algorithms for space situational awareness and astronomy applications. Responsibilities include researching SDA/SSA and photometry techniques, training and customizing machine-learning models, managing image-processing pipelines from sensor readout through cloud publication, generating simulated data, validating algorithms, and leading multidisciplinary research projects. The role also requires communicating technical findings to technical and non-technical stakeholders.

Develop and transition computer vision and image processing algorithms from prototypes to production-ready spacecraft systems. Design tracking, navigation, estimation, filtering, uncertainty-analysis, and optimization algorithms; validate performance through simulations, benchmarks, and real-world sensor data; assess camera and optical hardware impacts; and define APIs for integration with software and spacecraft architectures.

Design, prototype, and implement deep learning models for computer vision and SAR geospatial exploitation. Develop signal preprocessing, physics simulations, objective functions, and production-quality Python code. Present findings and support transition to operational systems for government customers.