Machine Learning Engineer, Reliability

About this role:

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 Turkey.

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 requirements part of how new models get onboarded to the platform

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

Qualifications/Nice to have:

• 5+ years of professional experience, with 2 year experience operating production ML or high-scale API systems, ideally with on-call ownership

• Experience working with and supporting diffusion models in production

• Strong systems fundamentals: distributed systems, networking, observability, and incident management

• Working knowledge of modern generative models (diffusion, transformers) and their failure modes in production

• Familiarity with security and safety practices for ML systems ,abuse prevention, content safety, or trust & safety engineering experience is a strong plus

• A bias toward automation, measurement, and blameless postmortems

• 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

What we offer at fal

• Interesting and challenging work

• Competitive salary and equity

• A lot of learning and growth opportunities

• Regular team events and offsites