Senior Machine Learning Engineer

Overview

Job Description Senior Staff Machine Learning Engineer

Company: Geico

Location: Austin, TX (Remote)

Salary: $103.60K - $142.20K/yr (Estimated pay)

Job Type: Full-time

Benefits: Retirement

Posted: 11 hours ago

GEICO is seeking a Senior 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.

• 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.
• 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.
• 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; 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.
• 10+ years of professional software development experience using at least two general-purpose languages.
• 10+ years architecting, designing, and building multi-component ML platforms leveraging open-source/cloud-agnostic components.
• 6+ years managing end-to-end SDLC for ML systems: version control, CI/CD, Kubernetes, testing, 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, 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.