Machine Learning Engineer, Reliability

Own availability, latency, and throughput SLOs across a large fleet of generative media model APIs serving production traffic at scale

Build the monitoring, alerting, and observability needed to catch ML-specific failures, output quality degradation, pipeline breakage, model regressions before customers do

Harden model deployment workflows with canary releases, shadow testing, automated rollbacks, and validation gates so new model versions ship safely

Drive the security posture of the model fleet: secure model serving, abuse and misuse detection, rate limiting, and protection against adversarial usage patterns

Operationalize safety systems for generative media, content moderation pipelines, safety classifiers, and guardrails that run reliably at inference time without compromising performance

Lead incident response for model API outages and degradations, run postmortems, and drive the engineering work that prevents recurrence

Improve capacity planning, autoscaling, and GPU fleet efficiency for inference workloads under highly variable traffic

Partner with model and infrastructure teams to make reliability, security, and safety requirements part of how new models get onboarded to the platform

You will have access to our massive GPU cluster for inference and evaluation

Some core technologies we use include Python, torch, diffusers, Kubernetes, and the fal Python SDK

You'll work alongside a team dedicated to quickly iterating on and deploying new AI breakthroughs — your job is to make sure that speed never comes at the cost of reliability

3+ years of professional experience, with 1 year experience operating production ML or high-scale API systems, ideally with on-call ownership