Reinforce Labs, Inc.
Operations Manager- Data Annotation
Reinforce Labs, Inc., Palo Alto, California, United States, 94306
About Us
We’re an AI-focused startup working with cutting‑edge models and safety‑critical use cases. A big part of our work relies on
high-quality data annotation
to train, evaluate, and monitor AI systems across complex domains (safety, fraud, compliance, content quality, etc.).
We’re looking for an
Operations Lead
to own our annotation operations end‑to‑end: managing projects, coordinating internal and external teams, and ensuring we deliver
high-quality labeled data on time, at scale .
Role Overview In this role, you will:
Be the
point person
for all data annotation projects.
Manage a team of annotators (external vendors).
Design, refine, and enforce
workflows, guidelines, and quality processes .
Partner with product, research, and engineering to turn vague requirements into
clear task specs and rubrics .
You’re the kind of person who loves structure, can keep many moving pieces aligned, and cares deeply about
quality, throughput, and reliability .
What You’ll Do Project & Workflow Management
Own planning and execution for multiple
annotation projects
at once (scope, timelines, staffing, and priorities).
Turn high‑level requirements into
clear task definitions, instructions, and edge‑case guidance .
Build and maintain
project plans
including milestones, SLAs, and communication cadences with stakeholders.
Team & Vendor Management
Manage a team of
annotators / reviewers
(internal and/or external).
Handle capacity planning, scheduling, and task assignment to hit deadlines.
Coordinate with
annotation vendors or agencies , ensuring they understand requirements and meet quality + throughput expectations.
Provide feedback, coaching, and training to improve annotator performance.
Quality, Process & Tooling
Define and iterate on
rubrics, guidelines, and golden sets
for consistent labeling.
Design and manage
QA workflows
(spot checks, double label, adjudication, calibration sessions).
Track and improve key metrics:
accuracy, agreement, throughput, cost per label , and SLA adherence.
Partner with the product/engineering team to improve
annotation tooling , dashboards, and automation.
Stakeholder Communication
Serve as primary contact for internal teams needing labeled data (research, product, T&S, etc.).
Provide
regular status updates : progress vs plan, blockers, quality metrics, and risks.
Gather feedback on label quality, edge cases, and evolving requirements; turn those into updated
guidelines and processes .
Continuous Improvement
Identify and implement process improvements to
increase speed, reduce errors, and lower costs .
Run experiments to optimize
task design, instructions, and QA strategies .
Help codify best practices into
playbooks and documentation
as we scale.
What We’re Looking For Experience
3–7+ years in
operations, project management, or program management , ideally in:
Data annotation / labeling
Trust & Safety operations
Customer support operations
Or another high‑volume, process‑driven environment
Experience managing
small to mid‑sized teams
and/or external vendors.
Prior work in
AI / ML, data labeling, or content moderation
is a strong plus.
Skills
Strong
project management
skills: planning, prioritizing, and keeping multiple tracks on schedule.
Excellent
written communication ; you can write clear guidelines and edge‑case docs.
Comfortable working with
metrics and dashboards
(e.g., spreadsheets, BI tools) to monitor performance.
Detail‑oriented and process‑mindful; you naturally look for ways to standardize and streamline.
Familiarity with
annotation tools
(e.g., Labelbox, Scale, Doccano, custom tools) is a plus, but not required.
Mindset
Ownership mentality: you feel responsible for outcomes, not just tasks.
Calm under pressure; you can navigate ambiguity and shifting priorities.
Collaborative; you work well with annotators, engineers, and leadership alike.
Bias toward action: you don’t just spot problems—you propose and test fixes.
Nice‑to‑Haves
Experience setting up
calibration tests, golden sets, and inter‑annotator agreement .
Background in
trust & safety, content policy, or compliance .
Exposure to
SQL or basic data analysis
for monitoring volumes and quality trends.
Experience in a
startup or early‑stage environment
where processes are still being built.
#J-18808-Ljbffr
high-quality data annotation
to train, evaluate, and monitor AI systems across complex domains (safety, fraud, compliance, content quality, etc.).
We’re looking for an
Operations Lead
to own our annotation operations end‑to‑end: managing projects, coordinating internal and external teams, and ensuring we deliver
high-quality labeled data on time, at scale .
Role Overview In this role, you will:
Be the
point person
for all data annotation projects.
Manage a team of annotators (external vendors).
Design, refine, and enforce
workflows, guidelines, and quality processes .
Partner with product, research, and engineering to turn vague requirements into
clear task specs and rubrics .
You’re the kind of person who loves structure, can keep many moving pieces aligned, and cares deeply about
quality, throughput, and reliability .
What You’ll Do Project & Workflow Management
Own planning and execution for multiple
annotation projects
at once (scope, timelines, staffing, and priorities).
Turn high‑level requirements into
clear task definitions, instructions, and edge‑case guidance .
Build and maintain
project plans
including milestones, SLAs, and communication cadences with stakeholders.
Team & Vendor Management
Manage a team of
annotators / reviewers
(internal and/or external).
Handle capacity planning, scheduling, and task assignment to hit deadlines.
Coordinate with
annotation vendors or agencies , ensuring they understand requirements and meet quality + throughput expectations.
Provide feedback, coaching, and training to improve annotator performance.
Quality, Process & Tooling
Define and iterate on
rubrics, guidelines, and golden sets
for consistent labeling.
Design and manage
QA workflows
(spot checks, double label, adjudication, calibration sessions).
Track and improve key metrics:
accuracy, agreement, throughput, cost per label , and SLA adherence.
Partner with the product/engineering team to improve
annotation tooling , dashboards, and automation.
Stakeholder Communication
Serve as primary contact for internal teams needing labeled data (research, product, T&S, etc.).
Provide
regular status updates : progress vs plan, blockers, quality metrics, and risks.
Gather feedback on label quality, edge cases, and evolving requirements; turn those into updated
guidelines and processes .
Continuous Improvement
Identify and implement process improvements to
increase speed, reduce errors, and lower costs .
Run experiments to optimize
task design, instructions, and QA strategies .
Help codify best practices into
playbooks and documentation
as we scale.
What We’re Looking For Experience
3–7+ years in
operations, project management, or program management , ideally in:
Data annotation / labeling
Trust & Safety operations
Customer support operations
Or another high‑volume, process‑driven environment
Experience managing
small to mid‑sized teams
and/or external vendors.
Prior work in
AI / ML, data labeling, or content moderation
is a strong plus.
Skills
Strong
project management
skills: planning, prioritizing, and keeping multiple tracks on schedule.
Excellent
written communication ; you can write clear guidelines and edge‑case docs.
Comfortable working with
metrics and dashboards
(e.g., spreadsheets, BI tools) to monitor performance.
Detail‑oriented and process‑mindful; you naturally look for ways to standardize and streamline.
Familiarity with
annotation tools
(e.g., Labelbox, Scale, Doccano, custom tools) is a plus, but not required.
Mindset
Ownership mentality: you feel responsible for outcomes, not just tasks.
Calm under pressure; you can navigate ambiguity and shifting priorities.
Collaborative; you work well with annotators, engineers, and leadership alike.
Bias toward action: you don’t just spot problems—you propose and test fixes.
Nice‑to‑Haves
Experience setting up
calibration tests, golden sets, and inter‑annotator agreement .
Background in
trust & safety, content policy, or compliance .
Exposure to
SQL or basic data analysis
for monitoring volumes and quality trends.
Experience in a
startup or early‑stage environment
where processes are still being built.
#J-18808-Ljbffr