MLOps / Infrastructure Engineer

Experience with vector databases or ANN systems, preferably within GCP (or AWS).

Experience serving LLMs or embedding-based models via API.

Experience with model monitoring, logging, and metrics platforms (e.g., Prometheus, Grafana, Sentry).

Familiarity with trust & safety infrastructure, abuse detection, or policy enforcement systems.

Salary Range:

$130K–$230K, depending on experience and location.

Bonus:

Performance-based annual bonus.

Professional Development:

Support for continuing education, conferences, or training.

This is a hands-on role responsible for deploying, monitoring, and scaling a real-time ML-powered content moderation system used to detect and triage abuse, threats, and edge-case language.

You’ll work closely with ML engineers, researchers, and clients to build infrastructure that makes high-performance models accessible and reliable in the wild.

Design and maintain cloud infrastructure (GCP or AWS) to support real-time model serving, data ingestion, and evaluation workflows.

Deploy and optimize APIs for low-latency access to ML models and embedding search systems.

Manage and optimize the end-to-end training data flow—from sourcing and cleaning datasets to preparing them for model consumption—ensuring accuracy, scalability, and efficiency.

Build observability tooling for production ML pipelines (monitor latency, error rates, request volumes, drift).