Staff Technical Lead for Inference & ML Performance

Job Application for Staff Technical Lead for Inference & ML Performance at fal Back to jobs Staff Technical Lead for Inference & ML Performance San Francisco Apply fal is pioneering the next generation of generative-media infrastructure. We're pushing the boundaries of model inference performance to power seamless creative experiences at unprecedented scale. We're looking for a Staff Technical Lead for Inference & ML Performance, someone who blends deep technical expertise with strategic vision, guiding a team to build and optimize state-of-the-art inference systems. This role is intense yet deeply impactful. Apply if you're ready to lead the future of inference performance at a fast-paced, high-growth frontier. Why this role matters You’ll shape the future of fal’s inference engine and ensure our generative models achieve best-in-class performance. Your work directly impacts our ability to rapidly deliver cutting-edge creative solutions to users, from individual creators to global brands.

What you'll do Day-to-day What success looks like Set technical direction. Guide your team (kernels, applied performance, ML compilers, distributed inference) to build high-performance inference solutions. fal’s inference engine consistently outperforms industry benchmarks in throughput, latency, and efficiency. Hands-on IC leadership. Personally contribute to critical inference performance enhancements and optimizations. You regularly ship code that significantly improves model serving performance. Collaborate closely with research & applied ML teams. Influence model inference strategies and deployment techniques. Seamless integration of inference innovations rapidly moves from research to production deployment. Drive advanced performance optimizations. Implement model parallelism, kernel optimization, and compiler strategies. Performance bottlenecks are quickly identified and eliminated, dramatically enhancing inference speed and scalability. Mentor and scale your team. Coach and expand your team of performance-focused engineers. Your team independently innovates, proactively solves complex performance challenges, and consistently levels up their skills. You might be a fit if you Are deeply experienced in ML performance optimization. You've optimized inference for large-scale generative models in production environments. Understand the full ML performance stack. From PyTorch, TensorRT, TransformerEngine, Triton to CUTLASS kernels, you’ve navigated and optimized them all. Know inference inside-out. Expert-level familiarity with advanced inference techniques: quantization, kernel authoring, compilation, model parallelism (TP, context/sequence parallel, expert parallel), distributed serving and profiling. Lead from the front. You're a respected IC who enjoys getting hands-on with the toughest problems, demonstrating excellence to inspire your team. Thrive in cross-functional collaboration. Comfortable interfacing closely with applied ML teams, researchers, and stakeholders. Nice-to-haves Experience building inference engines specifically for diffusion and generative media models Track record of industry-leading performance improvements (papers, open-source contributions, benchmarks) Leadership experience in scaling technical teams What you'll get One of the highest impact roles at one of the fastest growing companies (revenue is growing 40% MoM, we are 60x+ RR compared to last year, raised Series A/B/C within the last 12 months) with a world changing vision: hyperscaling human creativity. Sound like your calling? Share your proudest optimization breakthrough, open-source contribution, or performance milestone with us. Let's set new standards for inference performance, together. Apply for this job * indicates a required field First Name * Last Name * Preferred First Name Email * Phone Country Phone Resume/CV Attach Attach Dropbox Google Drive Enter manually Enter manually Accepted file types: pdf, doc, docx, txt, rtf Cover Letter Attach Attach Dropbox Google Drive Enter manually Enter manually Accepted file types: pdf, doc, docx, txt, rtf LinkedIn Profile Website Submit application Powered by Greenhouse