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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Machine Learning Engineer - **Company:** GEICO - **Location:** Palo Alto, CA, United States - **Experience:** Expert - **Salary:** $150,000.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), A/B Testing, Airflow, Amazon Web Services, Microsoft Azure, C Sharp (Programming Language), C++ (Programming Language), Continuous Integration, Data Stores, Data Warehousing, Elasticsearch, Fraud Prevention and Detection, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, NoSQL, Open Source Technology, Systems Development Life Cycle, Prometheus, Azure Machine Learning, Software Engineering, Data Streaming, Workflow Management Systems, Parquet, Feature Engineering, Large Language Models, Snowflake, Grafana, Apache Spark, Deep Learning, Kubernetes, Information Technology, Apache Flink, Cassandra, Apache Kafka, Spark Streaming, Machine Learning Operations, GPT, Software Version Control - **Published:** May 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=fdc49bd9ed3a3340 ## About the Role Do you have experience in Version control?, * Bachelor's degree or above in Computer Science, Engineering, Statistics, or related field. * 10+ years of professional software development experience using at least two general-purpose languages (e.g., Java, C++, Python, C#). * 10+ 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 * 6+ years managing end-to-end SDLC for ML systems: version control, CI/CD, Kubernetes, testing (unit/integration/data/ML eval), monitoring/alerting, production support. * 6+ 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, 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. ## Description * 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. What you'll do * Own 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., Great Rewards: We offer compensation and benefits built to enhance your physical well-being, mental and emotional health and financial future. * Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family's overall well-being. * Financial benefits including market-competitive compensation; a 401K savings plan vested from day one that offers a 6% match; performance and recognition-based incentives; and tuition assistance. * Access to additional benefits like mental healthcare as well as fertility and adoption assistance. * Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year. ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Is my AI alive but brain-dead? 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