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fal is building the generative media ecosystem that powers the next generation of AI products. The company provides infrastructure, tools, and model access enabling teams to move from idea to production at scale. fal serves developers and enterprises with a unified platform combining high-performance inference, orchestration, and observability.
In this role, you will own and improve CI/CD systems supporting fal's rapidly growing collection of ML models and inference pipelines. You'll work closely with the Applied ML and ML Performance teams to eliminate manual work, accelerate development cycles, and build reliable systems that allow ML engineers to ship new models and optimizations with confidence.
Key responsibilities include:
- Design, build, and maintain automated testing, validation, and deployment pipelines for ML models and inference services
- Dramatically reduce CI execution times through intelligent parallelization, caching, test selection, and efficient compute resource utilization
- Develop systems that test model outputs, detect quality regressions, and validate changes across different models, GPU architectures, and configurations
- Build continuous performance testing that detects regressions in inference latency, throughput, GPU utilization, and cost
- Create automated pricing and deployment checks to validate model pricing, billing configurations, API schemas, and deployments before production
- Extend agentic engineering workflows by developing AI-powered automation and agentic coding systems that diagnose CI failures, identify regressions, propose fixes, and streamline engineering workflows
- Build automated safeguards, deployment verification, rollback mechanisms, and monitoring to ensure new model releases are reliable
- Identify repetitive tasks across the ML team and build tools and systems that automate them
You'll have access to fal's massive GPU cluster for testing and development. The tech stack includes Python, PyTorch, Docker, GitHub Actions, NVIDIA GPUs, distributed GPU infrastructure, model serving and inference pipelines, observability and performance monitoring tools, and AI coding agents.
QUALIFICATIONS:
Required:
- 5+ years of software engineering background with proficiency in Python
- Experience building production infrastructure and developer tooling
- Experience designing and operating CI/CD systems using GitHub Actions or comparable technologies
- Deep understanding of automated testing, build systems, dependency management, caching, and parallel execution
- Experience with containerized workloads, Docker, and cloud infrastructure
- Ability to design reliable distributed systems and debug complex infrastructure failures
- Strong understanding of observability (logs, metrics, tracing, automated alerting)
- Passion for developer productivity and demonstrated ability to eliminate manual processes through automation
- Comfortable working independently, identifying high-impact problems, and building end-to-end solutions
Nice-to-have:
- Experience with ML infrastructure, PyTorch, GPU workloads, or model-serving systems
- Familiarity with NVIDIA GPU architectures and multi-GPU environments
- Experience building automated inference benchmarks or ML quality evaluation frameworks
- Experience with agentic coding tools such as Codex or Claude Code, or building custom AI engineering agents
- Experience optimizing CI/CD pipelines at scale, including distributed test execution and ephemeral compute environments
- Experience developing internal developer platforms or infrastructure-as-code tooling
