MLOps / Infrastructure Engineer

3–8 Years of Industry Experience | Remote | High-Impact

About the Role: We’re looking for an infrastructure-focused engineer who thrives at the intersection of machine learning, systems, and product delivery. 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.

In This Role, You Will:

• 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).
• Automate model deployment, retraining, and evaluation pipelines (CI/CD for ML).
• Work with ML engineers to package models for serving.
• Help manage vector databases and semantic search infrastructure (e.g., Pinecone, FAISS, Vertex Matching Engine).
• Ensure security, compliance, and uptime of infrastructure supporting safety-critical systems.

We’re Looking for Someone Who:

• Has 3–8 years of experience deploying machine learning systems or high-availability backend systems.
• Has shipped and maintained production infrastructure at scale, supporting ML workflows.
• Has experience with GCP, AWS, or similar platforms (including managed ML services).
• Is proficient in Terraform, Docker, Kubernetes, or similar infra tools.
• Understands performance tradeoffs in serving models and embedding search pipelines.
• Can work cross-functionally with ML, security, and product teams to deploy safely and iterate fast.
• Brings a builder's mindset and bias for ownership in ambiguous environments.

Nice to Have Experience With:

• 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.

What Success Looks Like in the First 3 Months:

• You’ve deployed and monitored a real-time ML inference system with well-defined observability.
• You’ve implemented an API with latency under 200ms for embedding or classifier-based inference.
• You’ve partnered with ML engineers to streamline deployment and retraining workflows.
• You’ve built logging and monitoring that gives insight into system performance and classifier behavior.

Compensation & Benefits:

• Salary Range: $130K–$230K, depending on experience and location.
• Bonus: Performance-based annual bonus.
• Professional Development: Support for continuing education, conferences, or training.
• Work Environment: Fully remote, U.S.-based.
• Health Benefits: Comprehensive health, dental, and vision coverage.
• Time Off: Generous PTO and paid holiday schedule.
• Retirement: 401(k) plan.