Lead Software Engineer, Full Stack
4 days ago
Doxel AI exists to bring computer vision to construction, so the industry can deliver what society needs to thrive. From hospitals to data centers, from foreman to VPs of construction, teams use Doxel to make better decisions everyday. In fact, Doxel has contributed to the construction of the facilities that provide many of the products and services you use everyday.
We have classic computer vision, deep learning ML object detection, a low-latency 3D web app, a complex data pipeline powering it all in the background. We're building out new workflows, analytics dashboards, and forecasting engines.
We're at an exciting stage of scale as we build upon our growing market momentum. Our software is trusted by Shell Oil, Genentech, HCA healthcare, Kaiser, Turner, Layton and several others. Join us in bringing AI to construction
The Role As a Lead Software Engineer, Full Stack, you will help define and build the next generation of Doxel's project management, commissioning, and field-enablement tools—systems trusted by the world's largest general contractors and owners delivering mega-projects at unprecedented scale.You'll design and deliver full-stack applications that handle massive volumes of complex field data—including videos, images, structured forms, and IoT device outputs—and transform them into fast, intuitive workflows for both office teams and field workers. You'll architect mobile and web experiences that make capturing, reviewing, and acting on this data effortless in environments where reliability, speed, and clarity matter.As a technical leader, you'll partner closely with product, design, backend, CV/ML, and platform engineering teams to turn Doxel's AI and data systems into beautifully executed user experiences. You'll provide technical leadership end to end shipping 0-1 projects, as well mentoring and coaching across engineering on best practices. What You'll Do
- Lead the design and development of full-stack applications powering project management, commissioning workflows, and field data capture.
- Build web and mobile tools that make it simple for field teams to upload videos, images, structured forms, IoT readings, and issue documentation.
- Develop intuitive, high-performance UIs that handle complex data streams and large-scale datasets.
- Architect backend services and APIs that support rapid data ingestion, processing, and review at enterprise scale.
- Collaborate cross-functionally with backend, CV/ML, product, design, and 3D visualization teams to deliver seamless end-to-end features.
- Drive full stack and mobile engineering best practices including testing, reliability, and performance optimization.
- Mentor and lead engineers establishing standards, reviewing architecture, and ensuring high-velocity execution.
- Contribute to technical planning, feature roadmaps, and long-term architectural decisions for Doxel's applications ecosystem.
- Champion usability and field readiness ensuring tools are simple, fast, and resilient in tough real-world construction environments.
- 10+ years of experience building and shipping full-stack web and mobile applications.
- Strong expertise with modern full-stack frameworks (e.g., , , , or similar).
- Strong backend engineering experience with Typescript, Python, or Java, including API development.
- Experience architecting applications that handle large-scale multimodal datasets (3d components, videos, images) and/or IoT data inputs.
- Deep experience building UX for field-facing or operationally critical workflows.
- Strong familiarity with cloud environments (AWS, GCP, or Azure) and containerized infrastructure.
- Skilled in designing domain-driven data models and working with OLTP/OLAP systems.
- Prior experience leading or mentoring small engineering teams.
- Experience with data warehousing tools such as Redshift, Snowflake, Databricks, or BigQuery.
- Background with offline-capable architectures and mobile-first data collection tools.
- Experience with 3D visualization ), geospatial data, or computer-vision-powered experiences.
- Exposure to ML/AI-driven feature development.
- Comfort with CI/CD pipelines, observability tooling, and automated testing best practices.
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