About the role

Senior MLOps Engineer focusing on applied MLOps for CreatorIQ, bridging data science and production-grade efficiency. Responsible for annotation workflows and cost-efficient model evaluation.

Responsibilities

Architect Annotation & Measurement Pipelines: Own the design and implementation of human-in-the-loop and auto-annotation workflows. You will set up systems (e.g., Label Studio) to establish tight confidence metrics and rigorous Inter-Annotator Agreement (IAA).
Drive Cost-Efficiency via Evaluation: Create the ground truth, golden sets, and by-model evaluation criteria that allows us to benchmark models and make informed data science decisions. Your work will directly impact margin by enabling us to deploy the right model for the right task.
Enforce Applied MLOps Standards: Implement "model-as-a-judge" frameworks, deterministic PII scrubbing, and other best practices within our production pipelines to ensure our model-driven products are robust and compliant.
Collaborate on Model Strategy: Work closely with our Data Science team to make applied generative decisions that result in stronger product outcomes and cost efficiencies.
Build & Integrate: Collaborate with engineering to integrate measurement loops into our broader infrastructure (AWS/GCP), ensuring our model lifecycle is automated and observable.

Annotation & Evaluation Expert: You have deep experience setting up annotation workstreams (using tools like Label Studio, Scale AI, or custom solutions) and know how to calculate and improve ground truth quality.
Applied MLOps Practitioner: You care about execution over policymaking. You have experience implementing model monitoring, versioning, and evaluation loops in a production environment.
Python Proficiency: You possess strong proficiency in Python, capable of writing the glue code, scripts, and API integrations necessary to bind our models to our application stack.
Model Optimization Mindset: You understand the trade-offs between model performance, latency, and cost. You are excited about the challenge of proving that a smaller model can do the job of a massive LLM.
Cloud & Infrastructure Fluency: You are comfortable working within AWS (Sagemaker, S3) and GCP (Vertex AI) environments and can interface effectively with DevOps to deploy your solutions.
Collaborative Leader: You can act as a force multiplier for our Data Science team, equipping them with the tools and data they need to move faster.

People: work with talented, collaborative, and friendly people who love what they do.
Guidance: utilize our learning platform to fully get the training and tools you’ll need to become successful here from your first day with us.
Surprise meal stipends: work from home can’t stop the enjoyment of someone else making a meal for you!
Work/life harmony: vacation, floating and set holidays, wellness allowance, and paid parental leave.
Whole Health Package: medical, dental, vision, life, disability insurance, and more.
Work from home stipend: to assist you in setting up a home office that works for you (or buy a new dog leash - your choice!).