Machine Learning Engineer, Reliability

🕒 July 28

🌐 India, Australia, +1 more countries – Remote

🇮🇳 India – Remote 🇦🇺 Australia – Remote 🇳🇿 New Zealand – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

⛑ DevOps & Site Reliability Engineer (SRE)

👻 Ghost score 24%

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fal

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

🔌 API

☁️ SaaS

Artificial Intelligence • API • SaaS

fal is a generative media platform for developers that provides access to a large gallery of production-ready image, video, audio and 3D generative models alongside serverless GPU inference and on-demand compute clusters for training and fine-tuning. The platform offers unified APIs and SDKs to call hundreds of open models or private weights, a high-performance inference engine, managed serverless GPU deployments, and dedicated clusters with modern NVIDIA hardware for large-scale training. fal targets developer and enterprise customers with features like SOC 2 compliance, private endpoints, usage analytics, and enterprise support, and is positioned for building, deploying, and scaling generative AI-powered products.

📋 Description

• 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

🎯 Requirements

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

🏖️ Benefits

• 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