# Senior or Staff Computer Vision Engineer

**Company:** [Hover](https://hotfix.jobs/companies/hover)
**Location:** San Francisco, CA, New York, NY
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, C++, Computer Vision, Deep Learning, 3D Reconstruction, Multi-View Geometry, Pose Estimation, Structure-From-Motion, Gaussian Splatting, Vlms
**Posted:** 2026-05-28

> Senior or Staff Computer Vision Engineer building production 3D reconstruction systems from real-world sensor data. Owns full lifecycle from research to deployment of CV/ML models for accurate property digital twins.

## Job Description

## Responsibilities
- Design, prototype, evaluate, and productionize advanced computer vision and deep learning systems for 3D reconstruction, scene understanding, semantic modeling, and structured property representation
- Work across the full lifecycle of applied research and engineering: identifying relevant academic and industry approaches, writing project plans and technical specs, building proof-of-concepts, training and evaluating models, analyzing reconstruction quality and performance, and integrating successful approaches into production systems
- Partner closely with 3D reconstruction, Product, Design, Engineering, modeling, infrastructure, graphics, frontend, and backend teams to turn ambiguous technical and customer needs into clearly scoped experiments and production capabilities
- Work may include: mobile capture, aerial imagery, multimodal sensor fusion, multi-view reconstruction, pose estimation, feature matching, dense geometry, model fitting, semantic understanding, volumetric/surfel/surface-based representations, CAD-quality structured outputs, Gaussian Splatting, VLMs, or foundation-model-style approaches for correspondence and 3D understanding

## Requirements
- 5+ years of experience in computer vision, machine learning, or 3D reconstruction within academic research and/or industry environments
- Hands-on experience with one or more areas of modern 3D vision and reconstruction (multi-view geometry, pose estimation, camera calibration, structure-from-motion, feature matching, dense reconstruction, structured 3D reconstruction, model fitting, optimization, scene understanding, semantic modeling, or structured 3D representation)
- Practical deep learning experience applied to 3D reconstruction, geometry, correspondence, pose, segmentation, semantic understanding, VLMs, or related spatial ML problems
- Strong software engineering skills in Python and/or C++
- Ability to prototype quickly, train models, evaluate approaches rigorously, and translate promising research into practical systems
- Experience designing experiments with clear metrics, baselines, datasets, evaluation plans, and go/no-go criteria
- Ability to write clear project plans, technical specs, research summaries, experiment reports, and production-readiness documentation
- Track record of technical ownership and the ability to independently drive projects from ambiguity through implementation and delivery
- Strong collaboration skills working with researchers, engineers, 3D modelers, infrastructure teams, product partners, and cross-functional stakeholders
- Strong product and engineering judgment balancing technical ambition, customer value, production constraints, scalability, reliability, timing, and business impact
- Master's or PhD in Computer Science, Machine Learning, Computer Vision, Robotics, Graphics, or a related field

## Staff-Level Additional Requirements
- Experience building and shipping production CV, ML, or 3D reconstruction systems at scale
- Ability to define technical direction, architectural strategy, evaluation methodology, and quality/performance standards for complex CV or ML systems
- Proven ability to lead initiatives from early-stage research and experimentation through production deployment, monitoring, and long-term system ownership
- Ability to operate effectively in ambiguous problem spaces and drive alignment across cross-functional stakeholders
- Strong communication and influence skills translating complex technical concepts for both technical and non-technical audiences
- Experience mentoring engineers and raising the technical bar through architecture reviews, technical leadership, experimentation frameworks, and engineering best practices
- Track record of translating research or technical innovation into measurable product, customer, or business impact

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