Mapping Analyst - Contract (6 months)

Simbe Robotics is a leading retail robotics company providing in-store intelligence solutions that help retailers optimize operations, improve shelf execution, and deliver valuable data insights. Our autonomous robots and multi-modal data collection systems are transforming how retailers manage inventory and make data-driven decisions.

Position Overview
Simbe is seeking a Mapping Analyst, on a contract basis, to own the integrity of the SLAM maps that power navigation for our deployed Tally robot fleet. Accurate maps are foundational to everything Tally does in-store — every scan, every route, every data point depends on the robot knowing exactly where it is. This role is responsible for detecting map degradation, rebuilding and patching maps using Simbe's proprietary tooling, and ensuring every store in the fleet has a clean, current, navigable map.

This is a hands-on technical role that blends remote robot operation, spatial data work, and image-level editing. The right candidate is comfortable remote-controlling a robot to capture a fresh SLAM map, just as comfortable stitching lidar scans from prior traversals into an existing map to patch problem areas, and precise enough to clean up and splice map imagery by hand when automated tooling isn't enough.

 

This is a 1099 independent contractor position for 6 months from the start date. Applicants must be able to work as a freelance in the United States. 

Map Health Monitoring Monitor map quality and drift indicators across the fleet. Identify stores where localization failures, delocalization events, or navigation errors point to a degraded or outdated map.

Remote SLAM Map Generation Remotely control robots in-store using proprietary tooling to drive fresh SLAM mapping traversals when a store's map needs to be rebuilt from scratch.

Map Patching from Historical Scans Pull lidar scans from previous traversals and use them to patch, extend, or correct sections of an existing map without requiring a full remap, minimizing on-site disruption.

Manual Map Editing Use photo/image editing software to clean up, align, and splice map segments — correcting artifacts, merging patched regions seamlessly, and removing transient obstacles or scan noise that automated processing didn't catch.

Quality Assurance Validate patched and newly generated maps against navigation performance before deployment, confirming that fixes resolve the underlying localization issue rather than masking it.

Tooling Feedback Work closely with Engineering to flag gaps in the mapping toolchain and inform improvements to automated map-patching and SLAM pipeline tools.

Incident Documentation Log mapping-related incidents, root causes, and resolutions in internal tracking systems, and contribute to playbooks for common map failure patterns.

Cross-Functional Coordination Partner with Robot Operations, Client Success, and Engineering to prioritize which stores need mapping attention and to communicate expected timelines for fixes.

1–3 years of experience in a technical, spatial-data, robotics, or GIS-adjacent role

Comfort with remote robot operation or teleoperation tools, or strong aptitude to learn proprietary robotics tooling quickly

Hands-on experience with image/photo editing software (e.g., Photoshop, GIMP, or similar) for precise pixel-level edits

Basic understanding of SLAM, lidar data, or occupancy-grid mapping concepts

Strong attention to detail — able to spot subtle map artifacts or misalignments that would otherwise cause navigation failures

Strong written communication for incident documentation and cross-team coordination

Comfort navigating a fast-paced startup environment with evolving tooling and responsibilities

Prior experience with SLAM algorithms, point cloud data, or robotics navigation stacks

Scripting experience (Python or similar) for batch-processing map files or automating repetitive patching tasks

Experience with Jira, Confluence, or similar operational tracking tools

Retail technology or fleet operations exposure is a plus

What We're Looking For
Someone who treats a degraded map as an active liability, not a backlog item — the instinct should be to patch it before it causes a delocalization event, not after. Candidates should be equally comfortable driving a robot remotely to capture new lidar data and sitting in an editor precisely correcting a map by hand, moving fluidly between the two as the situation demands.