Senior Software Engineer, Machine Learning Infrastructure & Automation
fal · Remote · Remote - USA ·
COMPENSATION
$170k – $230k
Listed on the employer's job board (Ashby)
- Senior level role
- Fully remote
- Python + PyTorch
- ML Infrastructure focus
- GPU cluster access
fal provides the infrastructure and tools for teams to deploy generative media products at scale. This role supports the ML team by building automation, infrastructure, and developer tooling to streamline the development, testing, and deployment of AI models.
You will own CI/CD systems for ML models and inference pipelines, focusing on eliminating manual work and accelerating development cycles. The work involves close collaboration with Applied ML and ML Performance teams to ensure reliable and efficient model shipping.
What you'll do
- Design and maintain automated testing and deployment pipelines for ML models
- Reduce CI execution times via parallelization, caching, and efficient compute use
- Develop systems to test model outputs and detect quality regressions
- Build continuous performance testing for latency, throughput, and GPU utilization
- Automate validation of model pricing, billing configurations, and API schemas
- Create AI-powered automation to diagnose CI failures and propose fixes
- Implement deployment safeguards, verification, and rollback mechanisms
- Identify repetitive tasks and build tools to automate engineering toil
What you'll need
- 5+ years of software engineering experience with strong Python skills
- Experience building production infrastructure and developer tooling
- Proficiency in designing CI/CD systems using GitHub Actions or similar
- Deep understanding of automated testing, build systems, and caching
- Experience with containerized workloads, Docker, and cloud infrastructure
- Ability to design distributed systems and debug complex failures
- Strong knowledge of observability including logs, metrics, and tracing
- Comfortable working independently on end-to-end solutions
Nice to have
- Experience with ML infrastructure, PyTorch, or model-serving systems
- Familiarity with NVIDIA GPU architectures and multi-GPU environments
- Background in building automated inference benchmarks
- Experience with agentic coding tools like Codex or Claude Code
- History of optimizing CI/CD pipelines at scale with distributed tests
- Experience developing internal developer platforms or IaC tooling
Stack
- Python
- PyTorch
- Docker
- GitHub Actions
- NVIDIA GPUs
What you get
- Health, dental, and vision insurance for US employees
- Regular team events and offsites
- Access to massive GPU cluster for testing and development
- Opportunities for learning and professional growth