Lead Software Engineer, 3D Computer Vision
New
Based in the United States, Pacific TimeFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- DockerKubernetesMachine LearningPyTorchC++TensorflowComputer Vision
Requirements
- 7+ years of experience in 3D computer vision, machine learning, or related fields.
- Degree in Computer Science, Engineering, or equivalent experience.
- Proven experience building and operating production-scale photogrammetry or 3D reconstruction systems.
- Strong expertise in core 3D vision domains such as SfM, MVS, feature matching, monocular depth estimation, SLAM, or mesh reconstruction.
- Demonstrated ability to balance classical computer vision approaches with modern learning-based methods.
- Experience shipping systems under real-world production constraints.
- Proficiency in C++ for performance-critical systems.
- Strong working knowledge of modern ML frameworks such as PyTorch or TensorFlow.
- Experience running large-scale ML experiments in cloud environments and working with containerized infrastructure (Docker, Kubernetes).
- Familiarity with CI/CD pipelines and engineering best practices.
- Strong communication skills.
- Experience mentoring engineers and interest in growing into broader technical leadership.
Responsibilities
- Lead the end-to-end design and optimization of the photogrammetry and 3D reconstruction pipeline, including SfM, MVS, feature matching, depth estimation, and mesh reconstruction systems.
- Drive integration of modern 3D approaches such as Gaussian splatting and foundation 3D models.
- Build, ship, and maintain scalable 3D computer vision systems optimized for speed, accuracy, and robustness.
- Own technical investigations from diagnosis through production rollout.
- Define and uphold technical standards for 3D vision work.
- Establish observability and monitoring frameworks for photogrammetry systems.
- Mentor engineers and expand into formal people leadership.
- Partner closely with leadership to translate roadmap priorities into technical delivery.
- Develop reusable engineering and debugging frameworks.
- Lead adoption of AI-first and agent-assisted development workflows.
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