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.

Requirements

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: 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.

Benefits

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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

machine learningdeep learninggradient boosted treessequence modelsTransformersCI/CDdata quality monitoringincident responseversion controlSDLC

Soft skills

leadershipmentoringcollaborationcommunicationorganizational skillsproblem-solvingtechnical documentationprioritizationstakeholder managementarchitectural design

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in EngineeringBachelor's degree in Statistics