ML Model Serving & High-Performance API Engineer

Black Forest Labs Inc.; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Travel reimbursement

Job summary

Black Forest Labs Inc. is seeking a Member of Technical Staff to bridge research breakthroughs and production systems.

You will own the bridge between research checkpoints and production-ready inference services, designing scalable APIs and high-performance serving. You will optimize GPU workloads, manage distributed systems, and collaborate with researchers to ship demos and live endpoints rapidly, across cloud infrastructures.

Qualifications

• Experience building and operating ML inference services in production.
• Design 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.

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.

Skills

API design

Distributed systems

Performance optimization

Async processing

Cloud platforms

Tools

Docker

Kubernetes

CUDA

Redis

Postgres

AWS

GCP

Azure

Job description

Black Forest Labs Inc. is seeking a Member of Technical Staff to bridge research breakthroughs and production systems.

You will own the bridge between research checkpoints and production-ready inference services, designing scalable APIs and high-performance serving. You will optimize GPU workloads, manage distributed systems, and collaborate with researchers to ship demos and live endpoints rapidly, across cloud infrastructures.