AI Data Operations Lead

Cinder•New York, NY

2d•Onsite

About The Position

Cinder provides a cutting-edge platform to protect the internet. Cinder safeguards the interaction layer: the front end of products that users engage — and sometimes abuse. Our AI agents, integrated workflow platform, and deep expertise power real-time integrity at the speed of innovation. We support some of the most important and innovative companies in the world, including OpenAI, Midjourney, and ElevenLabs. Cinder is backed by Accel and Y Combinator. We care deeply about being relentless, intentional, and empathetic. We hire gritty thinkers who set ego aside, aggressively solve customer problems, and get better every day. We’re building an enterprise-grade platform to make the internet safer and we need highly curious, hard-working self-starters. Cinder is hiring a AI Data Operations Lead to own the health, quality, and evolution of our Managed Services (MS) offerings. This role sits at the intersection of Trust & Safety operations, AI-augmented review systems, customer experience, and vendor management. Managed Services is a core and growing part of how customers experience Cinder. Today, that work spans content moderation, data labeling, red-teaming, and AI-assisted review workflows. This role exists to ensure we deliver consistently high-quality outcomes for customers, learn faster from operational data, and scale our approach thoughtfully as demand grows. This is a deeply hands-on role. You’ll own day-to-day operational performance for MS customers while also shaping how humans and AI work together inside our review systems. You’ll work closely with Product, Engineering, GTM, and external BPO partners — and you’ll represent Cinder directly to customers when quality, accuracy, or timelines matter most. This position is based in New York City, with an expectation of working in person most days.

Requirements

Have at least 4+ years of experience in operations roles within Trust & Safety, AI safety, content moderation, or data labeling.
Have directly managed BPO or vendor-based review/labeling teams at scale.
Are highly organized and comfortable owning complex, multi-threaded operational work.
Have experience performing root cause analysis on operational or quality issues.
Are comfortable getting into the weeds — tooling, data, training docs, edge cases — to understand what’s actually happening.
Can operate independently and take initiative in ambiguous environments.
Are comfortable using data (basic data science, metrics, dashboards) to monitor performance and partner with QA or customer teams.
Are excited about AI-augmented operations and believe human judgment + AI systems outperform purely manual approaches.

Nice To Haves

Experience spanning both Trust & Safety and data labeling workflows.
Experience with red-teaming or adversarial testing programs.
Background working on the customer side of BPO relationships.
Experience supporting customer-facing operations in high-stakes environments.

Responsibilities

Own the operational health, delivery, and outcomes for all Managed Services customers.
Lead quality programs across customers, including QA design, precision/recall analysis, accuracy tracking, and continuous improvement.
Identify root causes of quality regressions, missed SLAs, or labeling confusion — and drive fixes across tooling, training, or process.
Own escalation and risk tradeoffs when quality, safety, or customer timelines are at risk, exercising sound judgment in high-pressure situations.
Manage day-to-day relationships with BPO and data labeling partners.
Partner with vendors on staffing models, headcount planning, efficiency improvements, and performance management across time zones.
Review training materials, calibration processes, and reviewer feedback loops to ensure consistent, high-quality output at scale.
Write, test, and iterate labeling and review instructions in partnership with Product and customers.
Lead calibration processes with BPO partners to ensure consistent interpretation of guidelines and edge cases.
Work closely with Product and Engineering on AI deployments that support Managed Services customers.
Help design and refine workflows where AI augments — rather than replaces — expert human judgment.
Use operational data to inform model iteration, tooling improvements, and future product decisions.
Monitor pipeline health across human and AI-assisted workflows, identifying bottlenecks and making informed tradeoffs between quality, speed, and cost.
Act as a primary operational point of contact for Managed Services customers.
Navigate customer pressure around deadlines, errors, quality issues, and evolving requirements with calm judgment and clear communication.
Own prioritization and decision-making when information is incomplete and trade-offs are real.
Partner with GTM teams to bring Managed Services offerings to market.
Support upsells, renewals, and new customer conversations by translating operational credibility into clear value.