Machine Learning Engineer

IndeedRemote,
$118,000 - $245,000

About The Position

The Machine Learning Engineer I role partners closely with business partners across various functions to help execute strategic initiatives that increase revenue, drive operational scale, and improve efficiency for continuous growth. As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source packages and research publications, and creatively adapt models for solving business problems across Indeed. Work spans classical ML through LLM systems. You improve search and retrieval quality using real user signals. Execution includes experiments, iteration, and production reliability at scale. You collaborate with engineers, data scientists, and product teams to define problems, test approaches, and ship measurable improvements.

Requirements

  • Requires a Bachelor's degree in Computer Science, Mathematics, or Statistics, and a minimum of 2 years of related experience; or an advanced degree without experience
  • Experience building ML models in Python; solid software engineering and algorithms fundamentals
  • Experience developing backend services in Java/Kotlin for ML-driven systems and features
  • Experience writing clean, testable, and maintainable production code
  • Experience working with structured and unstructured data, including SQL for large-scale data querying, and building scalable data pipelines and features from data
  • Experience integrating ML models into search systems using engines such as OpenSearch or similar, with familiarity in container orchestration for deployment with senior guidance
  • Excellent understanding of model evaluation techniques, feature engineering, experiment design, and familiarity with LLM systems (RAG, embeddings, output evaluation)

Responsibilities

  • Build AI/ML systems for search, ranking, and recommendations
  • Develop LLM retrieval and generation workflows
  • Improve search and ranking relevance
  • Design metrics and run experiments
  • Monitor model quality, latency, and cost
  • Debug data, models, and system issues
  • Build training, inference, and eval pipelines

Benefits

  • quarterly bonuses
  • Restricted Stock Units (RSUs)
  • Paid Time Off policy
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