Sr Machine Learning Services Engineer

Adobe Adobe · Enterprise · San Jose, CA

Senior Machine Learning Services Engineer at Adobe to productionize AI and Generative AI capabilities into Adobe's creative products. This role focuses on building and operating large-scale cloud services, optimizing GPU-accelerated ML inference pipelines, and integrating new ML models into production systems. The position involves hands-on experience with model serving, inference optimization, computer vision, generative models, and agentic workflows.

What you'd actually do

  1. Design, build, and operate backend cloud services that power ML and Generative AI features across multiple Adobe products
  2. Build an agentic end to end pipeline for tech transfer modernization
  3. Architect and optimize GPU-accelerated ML inference pipelines for scalability, cost efficiency, and reliability in production
  4. Optimize ML models for production inference, including techniques such as quantization, pruning, graph optimization, batching, and hardware-aware tuning to improve latency, throughput, and cost
  5. Analyze and improve performance, quality, stability, and throughput of end-to-end AI workflows

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • NVIDIA Triton
  • TorchServe
  • ONNX
  • AIT
  • AOT
  • CUDA
  • Docker
  • Kubernetes
  • AWS
  • computer vision
  • generative models
  • agents for workflow automation and orchestration

Nice to have

  • quantization
  • pruning
  • graph optimization
  • batching
  • hardware-aware tuning
  • model validation
  • regression testing
  • quality evaluation
  • CI/CD pipelines
  • observability
  • monitoring
  • logging
  • incident response

What the JD emphasized

  • 5+ years of experience building, optimizing, and operating ML systems in production, including GPU-based workloads
  • Proven experience designing large-scale, reliable cloud services with strong performance and availability requirements
  • Strong background in model serving and inference optimization
  • Expertise in building agents for workflow automation and orchestration

Other signals

  • productionizing cutting-edge models
  • large-scale cloud services
  • GPU-optimized inference
  • millions of creators
  • customer-facing AI experiences