[Hiring] Staff Computer Vision Data Scientist @LineVision, Inc.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a Staff Computer Vision Data Scientist to join our mission-driven team and lead the development of advanced computer vision capabilities across our product ecosystem. This role blends deep technical expertise with mentorship and innovation to expand the frontier of what’s possible in real-world computer vision—particularly in complex, uncontrolled outdoor environments.

• Architect and own a sustainable, end-to-end machine learning training pipeline for CV models.
• Champion and deliver edge-deployable CV models that are robust, efficient, and optimized for low-power environments in remote areas.
• Mentor and coach data scientists and engineers in CV fundamentals, best practices, and emerging techniques.
• Develop, deploy, and maintain multiple CV models addressing a range of environmental and sensing contexts by gathering and curating novel ML training sets.
• Lead strategic initiatives involving multimodal data fusion (e.g., combining satellite, drone, LiDAR, multispectral, low-light, and RGB camera data).

Within the first 3 months:

• Fully onboard and assess current ML training pipelines and CV infrastructure.
• Begin developing a roadmap for a robust, sustainable training system.
• Build relationships across Data Science, Software, and Sensor Hardware teams to understand pain points and opportunities.

Within the first 6 months:

• Deliver a working MVP of a model training pipeline tailored to LineVision’s data constraints and goals.
• Guide team members through project-based mentoring and technical knowledge transfer.
• Pilot an early-stage edge-deployable CV model for field testing.

Within the first year:

• Stand up and document a scalable, production-grade CV training and deployment system.
• Ship multiple robust CV models in production, including at least one optimized for edge processing.
• Build a track record of successfully translating general product requirements into actionable tasks and clearly defined model performance metrics.
• Develop best-practice guidelines, model accuracy benchmarks, and reusable assets for future CV models.

Qualifications

• Deep experience building new CV systems from the ground up—not just optimizing components of legacy pipelines.
• Expertise in working with uncontrolled environmental imagery (e.g., outdoor, variable lighting, occlusion-heavy, and complex natural backgrounds).
• Strong knowledge of both classical and machine learning CV techniques, including feature matching, segmentation, edge detection, pose estimation, etc.
• Proficiency in Python and tools like OpenCV, scikit-image, Roboflow, and anomalib.

Requirements

• Creativity and practicality in building training datasets, including the use of public datasets, synthetic imagery, and domain adaptation techniques.
• Experience deploying ML models to edge devices with compute and power constraints.
• Strong physics intuition and ability to reason through sensor phenomena and imaging artifacts.
• Familiarity with AWS SageMaker, image labeling pipelines, and cross-sensor data fusion.
• Ability to communicate CV concepts clearly and build trust and confidence across scientific and executive teams.

Benefits

• Your talent, time, and energy will critically impact our success in accelerating our mission of growing grid capacity and resilience.
• You will hold broad responsibilities with high autonomy and trust in a communicative, collaborative, and fast-paced environment.
• You will be empowered to maintain work-life balance with unlimited PTO and a flexible remote work schedule.
• You will join a motivated and high-performing team working with cutting edge, patented technology to help solve key obstacles to the clean energy transition.

Interview Process

• Apply Online.
• Round 1: Phone screen (Recruiter)
• Round 2: Hiring Manager Interview
• Round 3: Panel Interviews
• Panel 1: Technical competency & experience
• Panel 2: Teamwork, culture fit, and customer presence
• Final Round: Leadership Team & Hiring Manager Sign-Off

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