Blue Signal LLC
Data Quality Manager
Location:
Remote (Chicago area, IL)
Employment type:
Full-time
Step into the driver's seat of an AI-first industrial tech disruptor that is redefining how factories see, think, and act. As the Data Quality Manager, you will architect the data pipelines that power state-of-the-art computer-vision models - turning raw imagery into production-grade insight. You will collaborate with machine-learning engineers, product leaders, and offshore labeling teams, owning both strategy and hands-on execution. Your work will have direct, measurable impact on product accuracy, customer adoption, and revenue growth.
Position Snapshot Domain: High-volume image/video data for vision AI solutions in manufacturing and robotics Team: Cross-functional ML engineering group (100 % remote, U.S. time-zones) Influence: First hire fully dedicated to data quality - build processes, choose tools, and scale vendor operations Compensation: Competitive base salary + bonus/equity, comprehensive benefits, and equipment stipend Travel: None (occasional team off-sites optional) Key Responsibilities
Direct the full lifecycle of large-scale computer-vision datasets - from raw capture and stratified sampling to release-ready benchmark packages and performance sign-off. Write and maintain annotation playbooks, class taxonomies, and version-controlled guidelines, managing change logs for every update. Lead external annotation partners and internal reviewers, setting throughput goals, quality KPIs, and daily calibration sessions. Design multi-layer QC workflows - consensus voting, blind audits, gold-task seeding, and IAA monitoring - to surface issues before they hit production. Partner with ML engineers on error analysis and active-learning loops, mining hard examples to boost model precision. Implement dataset lineage and versioning (e.g., DVC/lakeFS), producing immutable manifests for every release. Publish weekly dashboards covering label accuracy, coverage, review latency, and SLA attainment, driving continuous improvement. Champion diversity and bias mitigation, ensuring balanced, representative data for fair model performance. Collaborate with customer stakeholders to define edge cases, success metrics, and rollout roadmaps - communicating status, risks, and mitigation plans. Evaluate and extend best-in-class tooling (Label Studio, CVAT, etc.), integrating QC signals into the existing MLOps stack. Qualifications
Must-haves
Proven leadership of image/video annotation programs at production scale with strong vendor-management skills. Expertise in data-quality science - consensus/aggregation methods, IAA metrics, sampling, and gold-task programs. Hands-on experience with leading annotation platforms and custom workflow scripting. Proficiency in SQL and Python for data inspection, automation, and reporting. Stellar communication skills - able to translate complex model requirements into clear labeling instructions and QC checks. Nice-to-haves
Active-learning and data-augmentation strategy experience. Familiarity with label-aggregation algorithms and probabilistic labeling. Experience setting up data-versioning/lineage tools and integrating with MLOps dashboards (Weights & Biases, ClearML, etc.). Background in industrial or safety-critical environments is a plus. Why You'll Love It Here
Impact from Day 1 - Your decisions shape the accuracy and trustworthiness of every model shipped. Growth Path - Build the entire data-quality function with a trajectory toward data-ops leadership. Cutting-Edge Tooling - Work with leading open-source and commercial platforms while driving innovation in vision AI. Mission-Driven Team - Collaborate with world-class engineers who value curiosity, ownership, and excellence. About Blue Signal:
Blue Signal is an award-winning, executive search firm specializing in various specialties. Our recruiters have a proven track record of placing top-tier talent across industry verticals, with deep expertise in numerous professional services. Learn more at
bit.ly/46Gs4yS
1. The Core Purpose of the Role
This isn't a generic "data management" position - it's specifically focused on
computer-vision data quality
for AI/ML systems in
industrial or manufacturing environments .
That means the
primary mission
is to:
Ensure that every pixel of visual data used to train AI models is accurate, consistent, diverse, and traceable - so that models can make reliable real-world decisions.
This is a
high-leverage role
because the quality of labeled images and videos directly affects model performance, product accuracy, and ultimately customer satisfaction.
⚙ 2. What Kind of Person You're Looking For
A. Functional Expertise
You're seeking someone who:
Has
led large-scale image/video annotation programs , not just text/tabular data. Understands
data labeling science
- especially consensus methods, inter-annotator agreement (IAA), and gold standard testing. Is hands-on with tools like
Label Studio, CVAT, or SuperAnnotate , and can write scripts (Python, SQL) to automate QA or analysis. Can
interface with ML engineers
and understand feedback loops between labeling and model training. So, look for candidates who can talk about:
Dataset lifecycle management Quality control (QC) workflows Error analysis and model validation Vendor management (since this involves offshore labeling teams) B. Leadership and Process-Building
Because this is the
first dedicated data-quality hire , you want someone who can:
Design the entire data-quality system
- playbooks, taxonomy, and workflows from scratch. Manage external vendors and set KPIs. Scale operations while maintaining consistency and fairness in data. Communicate effectively between engineers, product managers, and customers. This is both
strategic (build systems)
and
tactical (run checks, write scripts) .
C. Cultural and Domain Fit
Given the company's positioning ("AI-first industrial tech disruptor"), you're targeting:
People who have worked at
AI startups, robotics companies, or industrial vision firms . Candidates comfortable in
fast-paced, ambiguous environments
who take ownership. Those motivated by
impact
- building foundational systems that directly improve model trust and adoption. 3. How to Identify a Strong Match (Signals to Look For)
CategoryWhat to Look ForRed Flags Experience Managed image/video labeling pipelines (not just data entry or BI)Generic data governance, no mention of computer vision Tools Familiar with Label Studio, CVAT, or similar; uses Python for QC scriptsLimited to Excel or Tableau Leadership Has led vendor teams or annotation contractorsOnly worked as an annotator or QC reviewer ML Awareness Understands how labeling impacts precision/recall metricsTalks about "accuracy" in vague business terms Mindset Curious, process-driven, collaborativeToo rigid, slow to adapt, or lacks communication clarity
4. How to Pitch It (if you're recruiting for this)
If you're talking to candidates, emphasize:
"You'd be the architect of our entire data-quality operation." "Your work directly affects model accuracy - this is high-impact, not background ops." "We're at the intersection of AI and manufacturing - your systems will touch real robots and vision models in production."
That tends to resonate strongly with technically minded data professionals who want ownership and impact.
Location:
Remote (Chicago area, IL)
Employment type:
Full-time
Step into the driver's seat of an AI-first industrial tech disruptor that is redefining how factories see, think, and act. As the Data Quality Manager, you will architect the data pipelines that power state-of-the-art computer-vision models - turning raw imagery into production-grade insight. You will collaborate with machine-learning engineers, product leaders, and offshore labeling teams, owning both strategy and hands-on execution. Your work will have direct, measurable impact on product accuracy, customer adoption, and revenue growth.
Position Snapshot Domain: High-volume image/video data for vision AI solutions in manufacturing and robotics Team: Cross-functional ML engineering group (100 % remote, U.S. time-zones) Influence: First hire fully dedicated to data quality - build processes, choose tools, and scale vendor operations Compensation: Competitive base salary + bonus/equity, comprehensive benefits, and equipment stipend Travel: None (occasional team off-sites optional) Key Responsibilities
Direct the full lifecycle of large-scale computer-vision datasets - from raw capture and stratified sampling to release-ready benchmark packages and performance sign-off. Write and maintain annotation playbooks, class taxonomies, and version-controlled guidelines, managing change logs for every update. Lead external annotation partners and internal reviewers, setting throughput goals, quality KPIs, and daily calibration sessions. Design multi-layer QC workflows - consensus voting, blind audits, gold-task seeding, and IAA monitoring - to surface issues before they hit production. Partner with ML engineers on error analysis and active-learning loops, mining hard examples to boost model precision. Implement dataset lineage and versioning (e.g., DVC/lakeFS), producing immutable manifests for every release. Publish weekly dashboards covering label accuracy, coverage, review latency, and SLA attainment, driving continuous improvement. Champion diversity and bias mitigation, ensuring balanced, representative data for fair model performance. Collaborate with customer stakeholders to define edge cases, success metrics, and rollout roadmaps - communicating status, risks, and mitigation plans. Evaluate and extend best-in-class tooling (Label Studio, CVAT, etc.), integrating QC signals into the existing MLOps stack. Qualifications
Must-haves
Proven leadership of image/video annotation programs at production scale with strong vendor-management skills. Expertise in data-quality science - consensus/aggregation methods, IAA metrics, sampling, and gold-task programs. Hands-on experience with leading annotation platforms and custom workflow scripting. Proficiency in SQL and Python for data inspection, automation, and reporting. Stellar communication skills - able to translate complex model requirements into clear labeling instructions and QC checks. Nice-to-haves
Active-learning and data-augmentation strategy experience. Familiarity with label-aggregation algorithms and probabilistic labeling. Experience setting up data-versioning/lineage tools and integrating with MLOps dashboards (Weights & Biases, ClearML, etc.). Background in industrial or safety-critical environments is a plus. Why You'll Love It Here
Impact from Day 1 - Your decisions shape the accuracy and trustworthiness of every model shipped. Growth Path - Build the entire data-quality function with a trajectory toward data-ops leadership. Cutting-Edge Tooling - Work with leading open-source and commercial platforms while driving innovation in vision AI. Mission-Driven Team - Collaborate with world-class engineers who value curiosity, ownership, and excellence. About Blue Signal:
Blue Signal is an award-winning, executive search firm specializing in various specialties. Our recruiters have a proven track record of placing top-tier talent across industry verticals, with deep expertise in numerous professional services. Learn more at
bit.ly/46Gs4yS
1. The Core Purpose of the Role
This isn't a generic "data management" position - it's specifically focused on
computer-vision data quality
for AI/ML systems in
industrial or manufacturing environments .
That means the
primary mission
is to:
Ensure that every pixel of visual data used to train AI models is accurate, consistent, diverse, and traceable - so that models can make reliable real-world decisions.
This is a
high-leverage role
because the quality of labeled images and videos directly affects model performance, product accuracy, and ultimately customer satisfaction.
⚙ 2. What Kind of Person You're Looking For
A. Functional Expertise
You're seeking someone who:
Has
led large-scale image/video annotation programs , not just text/tabular data. Understands
data labeling science
- especially consensus methods, inter-annotator agreement (IAA), and gold standard testing. Is hands-on with tools like
Label Studio, CVAT, or SuperAnnotate , and can write scripts (Python, SQL) to automate QA or analysis. Can
interface with ML engineers
and understand feedback loops between labeling and model training. So, look for candidates who can talk about:
Dataset lifecycle management Quality control (QC) workflows Error analysis and model validation Vendor management (since this involves offshore labeling teams) B. Leadership and Process-Building
Because this is the
first dedicated data-quality hire , you want someone who can:
Design the entire data-quality system
- playbooks, taxonomy, and workflows from scratch. Manage external vendors and set KPIs. Scale operations while maintaining consistency and fairness in data. Communicate effectively between engineers, product managers, and customers. This is both
strategic (build systems)
and
tactical (run checks, write scripts) .
C. Cultural and Domain Fit
Given the company's positioning ("AI-first industrial tech disruptor"), you're targeting:
People who have worked at
AI startups, robotics companies, or industrial vision firms . Candidates comfortable in
fast-paced, ambiguous environments
who take ownership. Those motivated by
impact
- building foundational systems that directly improve model trust and adoption. 3. How to Identify a Strong Match (Signals to Look For)
CategoryWhat to Look ForRed Flags Experience Managed image/video labeling pipelines (not just data entry or BI)Generic data governance, no mention of computer vision Tools Familiar with Label Studio, CVAT, or similar; uses Python for QC scriptsLimited to Excel or Tableau Leadership Has led vendor teams or annotation contractorsOnly worked as an annotator or QC reviewer ML Awareness Understands how labeling impacts precision/recall metricsTalks about "accuracy" in vague business terms Mindset Curious, process-driven, collaborativeToo rigid, slow to adapt, or lacks communication clarity
4. How to Pitch It (if you're recruiting for this)
If you're talking to candidates, emphasize:
"You'd be the architect of our entire data-quality operation." "Your work directly affects model accuracy - this is high-impact, not background ops." "We're at the intersection of AI and manufacturing - your systems will touch real robots and vision models in production."
That tends to resonate strongly with technically minded data professionals who want ownership and impact.