Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Test and maintain automated test systems and hardware, write and run automated and manual tests for a real-time image generator, triage logs, report bugs in JIRA, support engineers, and document runtime/integration tools.

Maintain and expand a proprietary automated test system for a 3D image generator: write Python automation and Jenkins scripts, create image-diff and performance tests, develop test plans, perform manual testing, document the system, and support cross-team communication.

Design and develop next-generation real-time graphics rendering engine and content-creation tools. Enhance and integrate art pipelines with shader systems for realistic, high-performance rendering. Lead and collaborate within a small core software team to ship production-quality simulation graphics under delivery deadlines.

Design, develop, test, optimize, and maintain C++ tools and Python scripts to automate and support the 2D/3D content creation pipeline. Collaborate with artists, engineers, producers, and testers to build pipeline integrations, plugins, and UI tools for content publishing.

Produce area-of-interest and wide-area geotypical feature population for the visual database using GIS tools, ML-assisted feature generation, QA, discrepancy resolution, and internal tool support. Collaborate with DAML and integration teams, prepare and preprocess training data, perform spatial analysis, and maintain feedback loops to improve ML-driven feature tools.

Lead research and development of physics-based RF and radar imaging models (SAR/ISAR, DBS), develop computational algorithms, validate sensor phenomenology, collaborate with engineers to integrate models into real-time C/C++ simulation software, produce technical documentation, and engage stakeholders to guide sensor-model improvements.

Create precise vector annotations and segmentation masks on geospatial imagery, validate and correct automated detections, maintain documentation and QA, support dataset pipelines, and assist ML teams with inference and model evaluation to produce production-ready training data.

Lead coordination, planning, and delivery of multiple software initiatives. Translate goals into plans, manage dependencies, risks, and communications, support testing and releases, triage field issues, and drive continuous improvement across cross-functional engineering, systems, and product teams.