Salary
$120,000–$160,000
Posted
Aug 4, 2026
Location
—
Last confirmed open
Sep 24, 2026

What this job asks for AI summary

A customer-facing applied AI engineer at a retail robotics company, responsible for configuring, tuning, and troubleshooting computer vision and data pipelines for retail deployments. The role spans production debugging, release validation, QA tooling, and translating recurring customer issues into product and engineering improvements. It suits a technically versatile engineer comfortable bridging data analysis, ML operations, and direct customer engagement.

Mid level · 3+ years · Full-time

Quick apply — this platform usually takes a CV and a few fields.

Must have (5)
PythonpandasSQLLinuxGit
Nice to have (4)
computer visionmachine learningOCRobject detection

Posted 2 times — it's one opening, so apply once.

We read this from the posting text with AI. Skim the description below before ruling yourself out.

How this req sits in the market our data

What gives you an edge
pandas10%

Rare in this occupation — lead with these, and say what you built with them.

What won't set you apart
Python51%SQL51%Linux45%

Most people in this occupation already list these. Still required — just not what gets you shortlisted.

What the occupation pays Median $138,970 (middle half $107,524–$175,762).

Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 6, 2026. It is a model, not a headcount.

Why we read it this way (4)

The role blends software/data engineering, ML operations, and customer solutions work — it could reasonably map to Data Scientists (15-2051) given the ML evaluation and computer vision focus, but the primary day-to-day work (pipeline configuration, tooling, production debugging, release validation) aligns more closely with Software Developers (15-1252).

Computer vision and ML skills appear only under 'Bonus Qualifications', so they are marked preferred despite being central to the company's product — the JD explicitly does not gate on them.

'Notebooks' and 'spreadsheets' are mentioned alongside pandas as acceptable data tools; these are not distinct named technologies and are omitted per guidelines.

No compensation range is stated in the posting.

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