Sr Machine Learning Engineer

Disney Disney · Media · Seattle, WA +2

Senior Machine Learning Engineer role focused on building services and models for ad selection in a high-throughput, low-latency microservices environment. The role involves applying ML solutions, developing LLM-based applications, using agent frameworks, and implementing RAG with vector databases to optimize ad delivery for Disney's media platforms.

What you'd actually do

  1. Designing, implementing, and testing logic to serve ads effectively adhering to various business criteria either with rule based or ML based solutions in a high-throughput, low-latency microservices environment.
  2. Enhancing systems’ observability with proper metrics, monitors, and alerts.
  3. Reading and understanding product user stories, and then translating them into actionable tasks for both frontend and backend components.
  4. Taking ownership of one or more of the team's domain areas.
  5. Understanding and using automated tools (AI) while adhering to company policy.

Skills

Required

  • BS or MS in Computer Science / Engineering or relevant work experience
  • 5+ years of software engineering, specifically ML experience
  • Proficiency with Java
  • Proficient in large scale ML/DL platforms and processing tech stack
  • Experience in large scale machine learning using tools such as scikit-learn, Spark MLLib, pytorch
  • Experience with LLM-based application development, using LLM-as-a-service providers such as AWS Bedrock, Azure Cognitive Services, or Google's Vertex AI
  • Experience using frameworks such as LangGraph, Crew AI, and strands sdk for developing agents
  • Experience with vector databases and retrieval-augmented generation
  • Demonstrable analytical / problem-solving skills
  • Strong knowledge of and experience implementing AI/ML technologies and applying mathematical and statistical concepts
  • Great communication, collaboration skills, and a strong teamwork ethic with both technical and non-technical audiences

Nice to have

  • SpringBoot and related Spring projects
  • Non-relational database technologies like DynamoDB
  • Caching datastores such as Redis, ValKey or MemCache
  • Data Streaming Mechanisms such as Apache Kafka and/or Kinesis
  • Cloud platforms such as AWS
  • Modern DevOps tools such as Terraform, Docker, and Kubernetes
  • Domain knowledge in the Ad Tech industry

What the JD emphasized

  • ML experience
  • LLM-based application development
  • agents
  • vector databases and retrieval-augmented generation

Other signals

  • ML models
  • LLM-based application development
  • agents
  • RAG
  • vector databases