--- title: 'Senior Software Engineer, Machine Learning Infrastructure & Automation at fal' canonical: 'https://feeny.ai/job/senior-software-engineer-machine-learning-infrastructure-automation-fal-united-yn92t5ee7v00' type: 'job' last_seen: '2026-10-15' --- # Senior Software Engineer, Machine Learning Infrastructure & Automation at fal - **Company:** fal - **Location:** United States - **Compensation:** $170k–$230k - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-10-09 - **Last confirmed live:** 2026-10-15 - **Apply:** https://jobs.ashbyhq.com/fal-ai/70031914-e873-451d-8d17-712bada15563 ## Job description fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products. As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on. About this role: Help fal's ML team move faster by building the automation, infrastructure, and developer tooling that makes developing, testing, and deploying generative AI models seamless. You'll own and improve the CI/CD systems supporting our rapidly growing collection of ML models and inference pipelines. Your focus will be on eliminating manual work, accelerating development cycles, and building reliable systems that allow ML engineers to ship new models and optimizations with confidence. This is a high-impact engineering role where you'll work closely with our Applied ML and ML Performance teams. You'll build everything from automated model validation and performance benchmarking to AI-powered development workflows that help engineers iterate faster. The ideal candidate thinks beyond traditional CI/CD and sees automation as a force multiplier for the entire engineering organization. What you’ll do: - Own ML CI/CD infrastructure Design, build, and maintain automated testing, validation, and deployment pipelines for our ML models and inference services. - Accelerate development cycles.Dramatically reduce CI execution times through intelligent parallelization, caching, test selection, and efficient use of compute resources. - Build automated model validation.Develop systems that test model outputs, detect quality regressions, and validate changes across different models, GPU architectures, and configurations. - Automate performance benchmarking. Build continuous performance testing that detects regressions in inference latency, throughput, GPU utilization, and cost. - Build automated pricing and deployment checks. Ensure model pricing, billing configurations, API schemas, and deployments are validated automatically before reaching production. - Extend our agentic engineering workflows. Develop AI-powered automation and agentic coding systems that automatically diagnose CI failures, identify regressions, propose fixes, and streamline engineering workflows. - Improve deployment reliability. Build automated safeguards, deployment verification, rollback mechanisms, and monitoring to ensure new model releases are reliable. - Eliminate engineering toil. Identify repetitive tasks across the ML team and build tools and systems that automate them, allowing engineers to focus on developing new models and improving performance. Qualifications/Nice-to-haves: - 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. Tech Stack: - Python, PyTorch, Docker, GitHub Actions - NVIDIA GPUs and distributed GPU infrastructure - Model serving and inference pipelines - Observability and performance monitoring tools - AI coding agents and automated engineering workflows - Access to fal's massive GPU cluster for testing and development What we offer at fal: - Interesting and challenging work - A lot of learning and growth opportunities - Health, dental, and vision insurance (US) - Regular team events and offsites ## U.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION: fal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law. ## About fal ## Company Overview - **One-liner**: fal provides a serverless generative media platform for developers, enabling lightning-fast inference for image, video, audio, and 3D AI models. - **Entity Type**: Private (Series D) - **Headquarters**: San Francisco, California, USA - **Founded**: 2021 - **Founders**: Burkay Gur, Gorkem Yurtseven ## Core Business - **Industry**: Technology, Information, and Internet (Generative AI Infrastructure) - **Target Customers**: B2B — developers, enterprises, and research labs building generative AI applications. - **Mission**: Build the world’s first generative media platform for developers, enabling real-time AI content creation at scale. ## Products & Services - **fal Inference Engine™**: A globally distributed serverless runtime optimized for large ML models on thousands of GPUs (H100, H200, B200). Up to 10x faster than alternatives, with 99.99% uptime and no cold starts. - **Model Gallery**: A library of 1,000+ production-ready generative media models (image, video, audio, 3D) accessible via a unified API and SDKs. - **Dedicated Compute Clusters**: On-demand or reserved GPU clusters for fine-tuning, training, or running custom models, with access to 1000s of Blackwell GPUs. - **fal Compute**: Hourly GPU pricing for dedicated VMs, starting at $1.89/hr for high-end hardware. ## Market Standing - **Valuation/Market Cap**: Not disclosed (most recent round: $140M Series D) - **Key Metric**: $140M Series D raised - **Total Funding**: At least $163M across rounds (Pre-Seed $9M, Series A $14M, Series D $140M) - **Notable Investors/Partners**: Sequoia Capital, Andreessen Horowitz (a16z), Bessemer Venture Partners, Kleiner Perkins, NVIDIA - **Growth Signals**: - Headcount grew 246% year-over-year to ~84 employees - Trusted by 1.5M+ developers and leading enterprises - Powers AI features for public companies and hypergrowth startups - SOC 2 compliant and enterprise-ready ## Competitive Advantages - **Serverless, globally distributed inference engine** purpose-built for generative media, eliminating GPU configuration and cold starts. - **Massive scale**: Capable of handling 100M+ daily inference calls with enterprise-grade reliability. - **Proprietary distributed data-feeding engine** and deep optimization for diffusion models. - **Deep ecosystem**: 1,000+ pre-integrated models and strong partnerships with NVIDIA and top-tier VCs. - **Developer-first experience**: Unified API, SDKs, and per-output pricing model reduce barriers to deployment. ## Strategic Focus - **Global infrastructure scaling**: Expanding GPU fleet across regions to meet accelerating demand for generative media. - **Enterprise adoption**: Strengthening compliance (SOC 2, SSO), private endpoints, and dedicated support for large customers. - **Generative Media Fund**: Investing in the next generation of companies building in the space. - **Talent acquisition**: Aggressively hiring across engineering, go-to-market, and operations to keep pace with growth. ## Why Work Here - **Engineering culture**: Fast-paced, high-agency environment focused on infrastructure and ML performance. Teams own critical systems from day one. - **Work model**: Primarily in-person at downtown San Francisco office. Remote opportunities available for exceptional candidates. - **Visa sponsorship**: Offered with relocation support to San Francisco. - **Benefits**: Competitive salary and equity, health/dental/vision insurance, regular team events and offsites. - **Growth trajectory**: Company is scaling rapidly (headcount +246% YoY) with ambitious goals to 1000x usage, offering significant career growth opportunities. - **Impact**: Work on the foundational platform powering generative AI for millions of users and leading enterprises. ## Sources 1. [fal.ai](https://fal.ai/) 2. [fal Careers](https://fal.ai/careers) 3. [fal Blog - Series D Announcement](https://blog.fal.ai/our-series-d-scaling-fal/) 4. [LinkedIn - fal](https://www.linkedin.com/company/features-and-labels) 5. [Greenhouse Jobs Board](http://job-boards.greenhouse.io/fal) ## Other roles at fal - [Influencer Marketing Manager](https://feeny.ai/job/influencer-marketing-manager-fal-san-francisco-vmqtx4fpnkxv) — San Francisco, CA - [Software Engineer, Applied Machine Learning](https://feeny.ai/job/software-engineer-applied-machine-learning-fal-san-francisco-rhz2qr697k2v) — San Francisco, CA - [Infrastructure Accounting Lead](https://feeny.ai/job/infrastructure-accounting-lead-fal-san-francisco-ddfpyd0njt0e) — San Francisco, CA - [Senior/Staff Software Engineer, Forward Deployed](https://feeny.ai/job/senior-staff-software-engineer-forward-deployed-fal-san-francisco-0jw2vrfsjj4j) — San Francisco, CA - [Software Engineer, Machine Learning-Backend](https://feeny.ai/job/software-engineer-machine-learning-backend-fal-san-francisco-d3tnnz2gd7xj) — San Francisco, CA - [Senior Software Engineer, Full Stack (Serverless)](https://feeny.ai/job/senior-software-engineer-full-stack-serverless-fal-san-francisco-cne8pnqchjjy) — San Francisco, CA - [Field Marketing Manager, Enterprise & ABM](https://feeny.ai/job/field-marketing-manager-enterprise-abm-fal-san-francisco-m05rec34ndeb) — San Francisco, CA - [Event Marketing Manager](https://feeny.ai/job/event-marketing-manager-fal-san-francisco-hqpptrw1s9j1) — San Francisco, CA - [Technical Content Marketing Manager, Infrastructure](https://feeny.ai/job/technical-content-marketing-manager-infrastructure-fal-san-francisco-4ezha0ywcwhk) — San Francisco, CA - [Product Marketer, Infrastructure](https://feeny.ai/job/product-marketer-infrastructure-fal-san-francisco-db7ckz5ww674) — San Francisco, CA