Machine Learning Engineer

Adobe Adobe · Enterprise · San Jose, CA

Machine Learning Engineer at Adobe to apply AI/ML to big data problems for customer understanding and optimization. Will build products addressing business challenges across the customer lifecycle, using statistical methods, predictive models, and optimization techniques for projects like attribution, media mix modeling, budget optimization, personalization, causal analysis, and time series analysis. Requires strong academic background, technical skills in applied statistics, ML, data analysis, and software development, with experience in large-scale datasets and scalable data handling.

What you'd actually do

  1. Develop predictive models on large-scale datasets to address various business problems with advanced statistical modeling, machine learning, and analytics techniques.
  2. Develop and implement scalable and efficient modeling algorithms that can work with large-scale data in production systems
  3. Collaborate with product management and engineering groups to develop new products and features.

Skills

Required

  • PhD or MS in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, or a related technical field or equivalent industry experience
  • 3+ years of hands-on experience in machine learning engineering or applied data science
  • Deep understanding of statistical modeling, machine learning, and deep learning
  • Proficiency in Python
  • experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
  • Solid experience with relational databases, SQL, and large-scale data pipelines (Spark, Pandas, or similar)
  • Excellent communication skills

Nice to have

  • Familiarity with working with large-scale datasets and scalable data handling approaches
  • Familiarity with statistical modeling and analysis tools (R, MATLAB, or equivalent)
  • Experience with LLMs, RAG pipelines, or generative AI
  • Familiarity with cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
  • Experience with A/B testing and Survival/time-to-event modeling
  • Knowledge of responsible AI practices (bias detection, model explain-ability)

What the JD emphasized

  • track record of taking these methods from experimentation to production
  • full ML lifecycle: feature engineering, model training, evaluation, deployment, and post-launch monitoring
  • Strong analytical and quantitative problem-solving ability

Other signals

  • apply AI and machine learning techniques to big-data problems
  • building various products that address challenging business problems
  • develop predictive models on large-scale datasets
  • develop and implement scalable and efficient modeling algorithms that can work with large-scale data in production systems
  • Experience with the full ML lifecycle: feature engineering, model training, evaluation, deployment, and post-launch monitoring