- Company Name
- GEICO
- Job Title
- Staff Machine Learning Engineer
- Job Description
-
**Job Title**
Staff Machine Learning Engineer
**Role Summary**
Senior individual‑contributor responsible for designing, building, and maintaining end‑to‑end machine learning solutions that enhance insurance claims operations. Involves data and feature engineering, model development, MLOps, and cross‑functional collaboration to deliver measurable business outcomes.
**Expectations**
- Deliver reliable, scalable, and cost‑efficient ML systems that meet business KPIs.
- Uphold rigorous standards for performance, security, governance, and model risk management.
- Mentor technical staff and promote adherence to enterprise ML best practices.
**Key Responsibilities**
- Architect machine‑learning platform: data/feature pipelines, experiment tracking, model registries, serving layers, and observability.
- Define and enforce reliability, performance, cost, security, governance, and risk standards across ML services.
- Design and implement models using classical ML (GBTs, regression, logistic) and deep learning (Transformers, sequence models) for severity/triage, claim outcome forecasting, and automation accelerators.
- Translate business objectives into measurable ML goals, experiment plans, and offline metrics.
- Build scalable training and inference pipelines; establish CI/CD for ML, automated evaluation, canary releases, and rollback.
- Implement monitoring for data quality, drift, fairness, latency, reliability, cost; lead incident response and post‑mortems.
- Collaborate with Claims, Product, Data Science, Platform/SRE, Security, and Legal/Compliance to gather requirements, scope, and prioritize backlogs.
- Own build‑vs‑buy decisions, tooling selection, and platform evolution strategy.
- Lead architectural improvements, reliability practices, and knowledge sharing across engineering teams.
- Mentor junior engineers and tech leads; codify best practices and contribute to enterprise‑wide ML standards.
- When applicable, design and evaluate retrieval‑augmented workflows, LLM prompts, and safety guardrails.
**Required Skills**
- Proficiency in at least two general‑purpose languages (Python, Java, C++, C#).
- Deep experience with ML libraries: scikit‑learn, XGBoost, LightGBM, PyTorch, TensorFlow, Hugging Face Transformers.
- Strong knowledge of data engineering and feature pipelines (Kafka, Spark/Flume, Delta/Parquet).
- Expertise in MLOps tools (MLflow, Seldon, Kubeflow, Airflow, Temporal).
- Familiarity with data warehouses/lakehouses (Snowflake, Snowflake SQL, Snowpark).
- Experience with distributed compute (Spark, Ray) and orchestration (Airflow, Temporal).
- Proficient in CI/CD, Git, Kubernetes, containerization, automated testing, and monitoring/alerting.
- Understanding of model governance, risk management, security, cost monitoring, and fairness/detection of data drift.
- Excellent communication, stakeholder management, and mentoring skills.
**Required Education & Certifications**
- Bachelor’s degree or higher in Computer Science, Engineering, Statistics, or a related field.
- Minimum 5 years professional software development experience, with 5 + years in architecting and operating multi‑component ML platforms.
- Relevant certifications (e.g., ML specialization, cloud architecture, security) are a plus but not mandatory.