About the role
fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products. As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on. This is a hybrid ML Engineering / Site Reliability Engineering role. You will own the reliability, security, and safety of fal's fleet of generative media model APIs, the production endpoints that thousands of developers and enterprises depend on every day. Your mission is simple to state and hard to do: keep a large, fast-moving fleet of image, video, and audio model APIs available, performant, secure, and safe at all times. You understand both how generative models work and how production systems fail. You're as comfortable debugging a misbehaving diffusion pipeline as you are tracing a latency regression through an inference stack, and you treat model-specific failure modes; degraded output quality, drift, unsafe generations, abuse patterns; as first-class reliability concerns alongside uptime and latency. This role will need to be based in India, Australia, or New Zealand
What you'll do 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