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Staff Machine Learning Engineer

LinkedIn GEICO Seattle, WA
Not Applicable Posted April 1, 2026 2 variants Job link
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Requirements
  • 5+ years of professional software development experience using at least two general-purpose languages (e.g., Java, C++, Python, C#).
  • 5+ years architecting, designing, and building multi-component ML platforms leveraging open-source/cloud-agnostic components:
  • Search/vector: ElasticSearch, Qdrant (as applicable to ML features and retrieval)
  • Data warehouse/lakehouse: Snowflake; familiarity with Parquet/Delta/Iceberg
  • Streaming: Kafka; plus Flink/Spark Streaming experience
  • Datastores: PostgreSQL; NoSQL (MongoDB, Cassandra)
  • Distributed compute: Spark, Ray
  • Workflow orchestration: Airflow, Temporal
  • 5+ years managing end-to-end SDLC for ML systems: version control, CI/CD, Kubernetes, testing (unit/integration/data/ML eval), monitoring/alerting, production support.
  • 5+ years working with cloud providers (Azure and/or AWS) in production ML contexts.
Preferred Skills
  • GenAI (e.g., LLMs and agentic workflows) may be leveraged where it augments ML systems; strong ML depth is primary.
  • Experience leveraging or fine-tuning LLMs (e.g., GPT, Llama, Mistral, Claude) to augment ML workflows, retrieval, or claims-facing tooling.
  • Hands-on with MLOps tooling: MLflow/Kubeflow, model registries, feature stores (e.g., Feast), experiment tracking, A/B testing and online evaluation frameworks.
  • Observability: Prometheus/Grafana, OpenTelemetry; SLO-driven operations and incident management.
  • Model safety, fairness, explainability (e.g., SHAP/LIME), and regulatory compliance; familiarity with model risk management practices.
  • Insurance/financial services domain experience: claims automation, fraud detection, risk modeling, subrogation, severity/triage, and regulatory stewardship.
  • Experience with high-throughput, low-latency inference and real-time feature pipelines.
Education
  • (Not required) – Bachelor’s degree or above in Computer Science, Engineering, Statistics, or related field.