# Software Engineer, Sensor Integration

**Company:** [Mach9](https://hotfix.jobs/companies/mach9)
**Location:** San Francisco, CA
**Role:** Data Engineering
**Skills:** Python, Parallel Computing, Distributed Systems, Gdal, Pdal, Postgis, Aws S3, Spark, Aws Batch, C++
**Posted:** 2026-06-24

> Build and maintain ingestion pipelines that convert large-scale geospatial sensor data (LiDAR, imagery) into standardized formats for ML training and product use. Requires strong Python skills, comfort with undocumented formats, and distributed systems experience.

## Job Description

## Responsibilities
- Own the ingestion pipelines that convert point clouds and imagery from hardware vendors into Mach9's standard internal format
- Reverse-engineer new vendor formats and updates — often working only with sparse or missing documentation — to expand what data Mach9 can take in
- Build agentic systems to automatically triage failures and reformat data
- Build automated checks and regression testing to guarantee the consistency of our data
- Optimize the performance of our processing and storage across massive geospatial datasets in the cloud
- Work directly with customers and partners to unblock critical customer projects

## Requirements
- Strong software development and debugging skills
- Experience building production software in Python
- Comfort operating with ambiguity — ability to dig into undocumented or messy data formats and reverse-engineer them
- Strong communication skills, with the ability to work across ML, product, and customer success teams
- A foundation in parallel computing or distributed systems
- Bachelor's degree in Computer Science, Engineering, or equivalent experience

## Nice-to-Haves
- Experience building agentic systems and setting up agent harnesses — orchestrating LLM-driven workflows for triage, debugging, or automated code patching
- Understanding of geospatial data formats (e.g., LAS/LAZ, COPC, E57, GeoTIFF, Shapefiles) and tooling (e.g., GDAL, PDAL, untwine, laz-perf)
- Expertise designing and managing data schemas and storage systems for geospatial data (e.g., Postgres/PostGIS, AWS S3)
- Experience with large-scale data processing frameworks and cloud platforms (e.g., Spark, AWS Batch)
- Familiarity with coordinate reference systems and transforms (CRS, WKT, pyproj, affine transforms)
- Experience building data versioning, lineage, or artifact-tracking systems
- Experience operating data pipelines that feed ML training and inference
- Familiar with C++

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