Machine Learning Engineer

Job Description

GEICO is seeking a Staff Machine Learning Engineer to help shape how Generative AI enhances customer and associate experiences across the enterprise. This is a hands‑on technical role who will be leading the strategy, architecture, and delivery of ML systems for the Claims organization: designing predictive models, robust data/feature pipelines, and production‑grade MLOps to drive measurable business outcomes.

Location:

Remote – Chevy Chase, MD. Full‑time. Retirement benefits available.

• Staff+ individual contributor role focused on end‑to‑end ML: data and feature engineering, modeling, deployment, monitoring, and continuous improvement.
• Partner with Claims Operations, Product, and Engineering to deliver ML capabilities such as severity/triage predictions, claim outcome forecasting, and automation accelerators.
• GenAI (e.g., LLMs and agentic workflows) may be leveraged where it augments ML systems; strong ML depth is primary.

• Work on the ML platform architecture: data/feature pipelines, experiment tracking, model registries, serving layers, offline/online evaluation, and observability.
• Define standards for reliability, performance, cost efficiency, security, governance, and model risk management across ML services.
• Lead design and implementation of models across classical ML and deep learning (e.g., gradient‑boosted trees, sequence models, Transformers for tabular/time‑series/NLP where relevant).
• Translate business goals into measurable ML objectives and experiment plans; ensure robust offline metrics and real‑world impact.
• Build scalable training and inference pipelines; establish CI/CD for ML, automated evaluations, canary releases, and rollback strategies.
• Implement monitoring for data quality, drift, fairness, latency, reliability, and cost; lead incident response and postmortems.
• Partner with Claims, Product, Data Science, Platform/SRE, Security, and Legal/Compliance to gather requirements, define scope, and prioritize backlogs.
• Maintain pragmatic technical roadmaps balancing business outcomes, release timelines, and engineering excellence.
• Own build‑vs‑buy decisions and tooling/service selection (speed to market, extensibility, TCO); guide platform evolution with clear architectural principles.
• Lead experienced engineers through complex platform implementations; drive system‑wide architectural improvements and reliability practices.
• Mentor engineers and junior tech leads; codify best practices; contribute to internal documentation and promote enterprise‑wide ML standards.
• Where appropriate, collaborate on retrieval‑augmented workflows, prompt/context management, and LLM evaluation and safety guardrails to complement ML systems.

• Bachelor’s degree or above in Computer Science, Engineering, Statistics, or related field.
• 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:
    Elastic Search, Qdrant (as applicable to ML features and retrieval)
  • Data warehouse/lakehouse:
    Snowflake; familiarity with Parquet/Delta/Iceberg
  • Streaming:
    Kafka; plus Flink/Spark Streaming experience
  • Data stores:
    Postgre
    
    SQL;
    No
    
    SQL (Mongo
    
    DB, 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.

• 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, Open Telemetry; 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.

Annual Salary $ - $