2026 University Graduate - Machine Learning Engineer

Adobe Adobe · Enterprise · Seattle, WA

Machine Learning Engineer role focused on building and optimizing scalable, high-performance generative AI inference pipelines and APIs for integration into Adobe's consumer products. The role involves optimizing models for latency and throughput, productionizing generative models, and developing enterprise-scale systems for customization and serving.

What you'd actually do

  1. Core GenAI Services Development: Design and develop APIs and services that integrate a wide range of generative models into Adobe's flagship products, ensuring seamless user experiences.
  2. ML Pipeline Optimization: Build and optimize GPU-accelerated pipelines for model training and inference, prioritizing performance, scalability, and reliability across enterprise-scale deployments.
  3. Model Integration & Productization: Collaborate with Adobe Research and model developer teams to implement inference strategies and productionize modern generative models.
  4. Enterprise-Scale Systems: Design and build ML workflows for enterprise-scale model customization, serving, and ecosystem integration that handle massive user loads.
  5. Develop predictive models on large-scale datasets to address various business problems with advanced statistical modeling, machine learning, and analytics techniques.

Skills

Required

  • PhD or MS degree in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics or equivalent experience
  • 0-2+ years of experience in specific skill/field(s)
  • Deep understanding of statistical modeling, machine learning, deep learning, or data mining concepts
  • Proficient in one or more programming languages such as Python, Scala, Java and C
  • Familiar with one or more machine learning or statistical modeling tools such as R, Matlab and scikit learn
  • Knowledge and experience of working with relational databases and SQL
  • Strong analytical and quantitative problem-solving ability
  • Outstanding communication and relationship skills

Nice to have

  • Familiarity with working with large-scale datasets and big data techniques

What the JD emphasized

  • Generative AI
  • inference pipelines
  • optimize models for latency
  • enterprise-scale deployments
  • productionize modern generative models
  • enterprise-scale model customization
  • serving

Other signals

  • Generative AI
  • inference pipelines
  • optimize models for latency
  • APIs and ecosystems
  • enterprise-scale deployments
  • productionize modern generative models
  • enterprise-scale model customization
  • serving
  • ecosystem integration