Jobs at Cartesian

PhD intern will research and develop machine learning, perception, sensor fusion, and signal-processing algorithms for indoor positioning and spatial AI. Responsibilities include experimenting with real-world deployment data, analyzing failures, improving production ML pipelines, validating models, and collaborating with engineering and product teams to ship enterprise features. The role is hands-on, product-focused, and involves research in production rather than public publication.

Conduct research in sensor fusion and spatial AI; develop indoor positioning and spatial perception algorithms; run experiments on customer deployments; analyze failures; improve production machine learning and signal processing pipelines; and collaborate with engineering and product teams to ship enterprise features. The role is an in-person, full-time postdoctoral position requiring a PhD or imminent PhD completion.

Conduct market, customer, competitor, and partner research; synthesize strategic recommendations; support financial modeling, reporting, operational processes, customer success, partner relations, product demos, onboarding, and fundraising. Collaborate closely with operations leadership and founders on high-impact company initiatives in an in-person startup environment.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.

Full-time, on-site software engineering internship supporting customer integrations, platform improvements, research tooling, security, UI/UX, cloud optimization, and testing. The intern will own a defined project, collaborate with senior engineers and research teams, iterate on feedback, and ship code to real customers using the company’s backend, mobile, cloud, data, infrastructure, and machine-learning technologies.

Build Android SDKs and end-user applications that collect sensor data, provide real-time feedback, and support indoor item positioning. Integrate mobile software with RFID scanners, barcode readers, peripheral hardware, and third-party systems. Drive complex projects end to end, contribute across the product lifecycle, and collaborate autonomously with business stakeholders. Emphasize maintainable, performant mobile architecture, correctness, intuitive user experiences, and potentially backend development, testing, and enterprise SDK support.

Build secure, scalable software products and infrastructure with an emphasis on application security, access control, threat modeling, audits, monitoring, CI/CD security, cloud infrastructure, and secure development processes. Develop Python web services, APIs, dashboards, distributed ML systems, and infrastructure using Kubernetes, Helm, and Terraform. Collaborate autonomously across the business, drive complex projects end to end, and establish security-by-default engineering practices.

Build end-to-end mobile, web, backend, data, and infrastructure features for Cartesian’s indoor inventory positioning platform. Develop applications for retail associates, internal research, and business teams; create APIs, dashboards, datasets, and scalable services. Manage cloud infrastructure with Kubernetes, Helm, and Terraform, while improving CI/CD and architecture. Collaborate directly with customers and users, test on physical devices, and autonomously drive projects from prioritization through delivery.

Design, develop, and deploy machine learning models for indoor positioning and perception. Optimize model architectures, create real-world benchmarking tools and datasets, translate research into production pipelines, and collaborate with engineering and product teams to ship enterprise features. The role requires advanced expertise in time-series machine learning, transformer and probabilistic models, production coding, and research publication, with opportunities to apply computer vision, wireless localization, sensor fusion, and mobile ML deployment.