Senior ML Infra Engineer: AI CI/CD & Automation at Scale

OpenKrill; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Team events

Job summary

fal's ML team builds automation, infrastructure, and developer tooling to speed model development, testing, and deployment. You’ll own CI/CD systems and pipelines for ML models and inference services, reducing manual work and accelerating cycles.

You’ll work withApplied ML and ML Performance teams to enable automated validation, performance benchmarks, and AI-powered workflows, turning automation into a force multiplier for the engineering org.

Qualifications

• 5+ years of software engineering with Python and production infrastructure experience.
• Experience designing and operating CI/CD systems (GitHub Actions or similar).
• Deep knowledge of automated testing, build systems, caching, and parallel execution.
• Experience with containerized workloads and cloud infrastructure (Docker, clouds).
• Ability to design reliable distributed systems and debug infra failures.
• Strong observability: logs, metrics, tracing, alerts.
• Passion for developer productivity and automation.
• Independent work style, high-impact problem solving.
• Experience with ML infra, PyTorch, GPUs, or model-serving systems.
• Familiarity with NVIDIA GPU architectures and multi-GPU environments.
• Experience building ML quality evaluation frameworks or benchmarks.
• Experience with AI coding agents or agentic tools.
• CI/CD optimization at scale, distributed test execution, temporary compute environments.
• Experience developing internal dev platforms or IaC tooling.

Responsibilities

• Own ML CI/CD infrastructure, design, build, and maintain pipelines for ML models and inference services.
• Accelerate CI times via parallelization, caching, and test selection.
• Build automated model validation across models, GPUs, and configurations.
• Automate performance benchmarking for latency, throughput, and cost.
• Build automated pricing and deployment checks before production.
• Extend AI-powered automation and agentic coding workflows.
• Improve deployment reliability with safeguards and rollback mechanisms.
• Eliminate repetitive toil by building internal tooling.

Skills

Python

CI/CD

GitHub Actions

Automated testing

Docker

Cloud infrastructure

Distributed systems

Observability

Automation

ML infrastructure

PyTorch

GPU workloads

Agentic coding tools

Code design

Tools

Docker

GitHub Actions

Job description

fal's ML team builds automation, infrastructure, and developer tooling to speed model development, testing, and deployment. You’ll own CI/CD systems and pipelines for ML models and inference services, reducing manual work and accelerating cycles.

You’ll work withApplied ML and ML Performance teams to enable automated validation, performance benchmarks, and AI-powered workflows, turning automation into a force multiplier for the engineering org.