Senior Computer Vision Researcher

Following selection criteria will be followed -

• 5-15 YOE
• Worked on at least 1 Computer vision problem statement in the past 5 years
• Have built, trained and deployed models on production before
• Comfortable with WFO in Bangalore or Noida
• Happy to read research papers day in day out and train on terabytes of image data on big H100 clusters
• preferably tier-1 college ( IISc, IITs, NITs, BITS and others)

About Us

SiteRecon is a B2B SaaS platform transforming property measurements for landscapers and snow removal contractors in the $176B US market. Using advanced AI on high-resolution aerial imagery, we’ve reduced measurement time from 1 week to 1 day, aiming for 1 hour with upcoming AI advancements.

Role Overview

We seek a skilled engineer to evaluate, enhance and / or transform our computer vision infrastructure from traditional CNN architectures to cutting-edge transformer-based models. This role demands expertise in model design, training, and optimization.

You’ll have access to :

This is a frontier challenge in computer vision and GIS. While solutions like Meta’s SAM offer basic raster segmentation, they lack the precision for creating high-fidelity, topologically consistent vectors essential for practical GIS applications. SAM struggles with occlusions (e.g., shadows, tree canopies) and produces "blobs" rather than architect-quality outputs.

Our approach focuses on solving a constrained problem : using the world’s highest-resolution aerial imagery (7 cm) over U.S. urban areas with logical, repeatable patterns. By tackling this focused challenge, we aim to develop scalable templates to generalize automated extraction for broader GIS applications, similar to Waymo’s strategy in self-driving technology.

Key Responsibilities

Required Technical Skills

Required Experience

Desired Qualities

What Sets You Apart

Why This Role Matters

Every day without improved segmentation costs us real business opportunities. We need someone who moves fast, thinks systematically, and delivers production-ready improvements quickly.