Top Tech Jobs & Startup Jobs

Develop and productionize large-scale computer vision and machine learning solutions for satellite and Earth-observation data. Responsibilities include data preparation, model development, validation, deployment, monitoring, optimization, metric definition, stakeholder communication, applied research, and mentoring. The role focuses on low-latency, high-throughput geospatial intelligence systems using multimodal and temporal data, while advancing state-of-the-art techniques and supporting product delivery.

Lead quality assurance and quality control for space optical and electronic payloads. Conduct hardware inspections, oversee thermal vacuum, vibration, shock, and EMI/EMC testing, enforce cleanroom and ESD protocols, maintain NCR, RCA, CAPA, traceability, and configuration records, interpret aerospace standards, and audit suppliers. The role also supports component upscreening, failure analysis, qualification processes, and compliance throughout payload development and production.

Build and maintain scalable test automation frameworks using Playwright, Cucumber, Python, and Java. Automate web, mobile, API, data, and end-to-end workflows; integrate testing into CI/CD pipelines; perform functional, regression, performance, and data validation testing; investigate production issues; and drive quality strategies, metrics, release readiness, and engineering best practices across the SDLC.

Lead the end-to-end development of Earth observation imaging payloads, including payload architecture, optical and opto-mechanical design, requirements, integration, verification, testing, image quality analysis, launch preparation, and in-orbit calibration. Coordinate subcontractors, collaborate with satellite platform teams, manage performance budgets, and drive feasibility studies and future optical payload technology programs.

Design, build, and operate multi-cloud Kubernetes infrastructure supporting ML platforms, LLM inference, data pipelines, and developer tooling. Own Terraform and Helm automation, GitOps deployments, cluster operations, observability, reliability, FinOps, security governance, and internal platform standards. Partner with engineering and data science teams to scale workloads, optimize cloud costs, define SLOs, and lead incident response across AWS, GCP, and Azure.