Senior Software Engineer, Machine Learning Infrastructure – Automation

fal

. Own, design, build, and maintain ML CI/CD infrastructure for automated testing, validation, and deployment pipelines for ML models and inference services .

Posted 10/9/2026full-timeSan Francisco • California • United StatesSenior💰 $170,000 - $230,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining ML CI/CD infrastructure, with a strong focus on automation, performance testing, and developer productivity. Proficient in Python and experienced in designing reliable distributed systems and optimizing CI/CD pipelines at scale.

Highest-signal resume keywords

Python ProficiencyCI/CD System DesignAutomated TestingML Infrastructure ExperienceDocker and Cloud Infrastructure

ATS Keywords

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Hard Skills

CI/CD InfrastructureAutomated TestingPerformance TestingDistributed Systems DesignDependency ManagementParallel ExecutionML Quality EvaluationAgentic Coding ToolsInfrastructure-as-CodeNVIDIA GPU Architectures

Soft Skills

Problem IdentificationIndependent WorkCollaboration

Tools & Technologies

GitHub ActionsDockerCloud InfrastructurePyTorchCodexClaude Code

Industry Keywords

ML ModelsInference ServicesAutomated ValidationDeveloper ProductivityCompute Resources

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerPythonPyTorch

About the role

Key responsibilities & impact

Own, design, build, and maintain ML CI/CD infrastructure for automated testing, validation, and deployment pipelines for ML models and inference services
Reduce CI execution times through intelligent parallelization, caching, test selection, and efficient use of compute resources
Develop automated systems to test model outputs, detect quality regressions, and validate changes across models, GPU architectures, and configurations
Build continuous performance testing for inference latency, throughput, GPU utilization, and cost
Automate validation of model pricing, billing configurations, API schemas, and deployments before production
Develop AI-powered automation and agentic coding systems to diagnose CI failures, identify regressions, propose fixes, and streamline engineering workflows
Build deployment safeguards, verification, rollback mechanisms, and monitoring
Identify repetitive ML-team tasks and build automation tools to eliminate engineering toil
Collaborate closely with Applied ML and ML Performance teams
Build infrastructure, tools, and model access that help generative media teams move from idea to production at scale

Requirements

What you’ll need

5+ years of software engineering background with proficiency in Python and 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 working with containerized workloads, Docker, and cloud infrastructure
Ability to design reliable distributed systems and debug complex infrastructure failures
Strong understanding of observability, including logs, metrics, tracing, and automated alerting
A passion for developer productivity and a demonstrated ability to eliminate manual processes through automation
Comfortable working independently, identifying high-impact problems, and building end-to-end solutions
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
Must be legally authorized to work in the United States
Must answer whether visa sponsorship is required to work in the United States
Must be able to meet the requirement for in-person attendance five days per week in downtown San Francisco

Benefits

Comp & perks

Offers equity
Interesting and challenging work
A lot of learning and growth opportunities
Health, dental, and vision insurance (US)
Regular team events and offsites
Access to fal's massive GPU cluster for testing and development