Member of Technical Staff - Model Serving / API Backend Engineer

Black Forest Labs • San Francisco, CA

8d • Hybrid

About The Position

Our research team moves fast. Models improve weekly. New capabilities emerge constantly. What slows us down is not model quality—it’s productionization. Without this role: Research checkpoints sit longer before becoming usable APIs Inference is slower than it needs to be APIs struggle under load Demos don’t reflect the true potential of our models This role removes the bottleneck between frontier research and production reality. Once hired, researchers ship faster, demos launch faster, and customers experience models at their best. You will own the bridge between research breakthroughs and production systems. Turn research checkpoints into production-ready inference services Design and maintain high-performance APIs serving millions of requests Optimize inference latency and throughput across GPU infrastructure Build scalable serving architectures that handle unpredictable traffic Improve reliability, monitoring, and observability across model-serving systems Prototype and ship demos that showcase new capabilities in days, not weeks Collaborate closely with researchers to move from idea to live endpoint rapidly This role spans backend systems, GPU performance, and production ML serving.

Requirements

• You’ve built and operated systems at meaningful scale.
• You understand the difference between a research prototype and a production system.
• You are comfortable navigating ambiguity, making tradeoffs, and improving systems under real-world constraints.
• Strong judgment around performance, reliability, and cost tradeoffs
• Experience scaling APIs or ML systems under load
• Comfort working in fast-moving, research-adjacent environments
• Ownership from system design through debugging and deployment
• Building and operating ML inference services in production
• Designing scalable API architectures with async processing
• Optimizing GPU workloads (batching, quantization, compilation, CUDA)
• Managing distributed systems and task queues under variable load
• Implementing monitoring and observability for production ML systems
• Debugging performance bottlenecks across model, infrastructure, and network layers

Nice To Haves

• Real-time or low-latency inference systems
• TensorRT, reduced precision, layer fusion, or model compilation techniques
• Frontend demo tooling (Streamlit, Gradio, React)
• CI/CD and automated testing for ML systems
• Security best practices for API and model serving

Responsibilities

• Turn research checkpoints into production-ready inference services
• Design and maintain high-performance APIs serving millions of requests
• Optimize inference latency and throughput across GPU infrastructure
• Build scalable serving architectures that handle unpredictable traffic
• Improve reliability, monitoring, and observability across model-serving systems
• Prototype and ship demos that showcase new capabilities in days, not weeks
• Collaborate closely with researchers to move from idea to live endpoint rapidly

Benefits

• We’ll cover reasonable travel costs to make this possible.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed