Staff Machine Learning Engineer

Geico Insurance•Palo Alto, CA

35d•$130,000 - $260,000

About The Position

At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers' expectations while making a real impact for our company through our shared purpose. When you join our company, we want you to feel valued, supported and proud to work here. That's why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers. Staff Machine Learning Engineer Overview: As a Staff Machine Learning Engineer, you will be the overall tech lead of a single AI/Machine Learning team, responsible for the tech design and tech health of the team. You will build and architect scalable and reliable AIML solutions that align with the company's tech paved path and stakeholder requirements. This role requires a minimum of 6 years of relevant experience.

Requirements

Bachelor's degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related field; an advanced degree (master's or Ph.D.) is highly desirable
At least 6 years of hands-on experience in machine learning and software engineering.
Deep proficiency in programming languages such as Python, Java, or similar, with a strong emphasis on coding excellence.
Proficiency in AIML frameworks such as TensorFlow, PyTorch, Scikit-learn, Langchain, langraph, etc.
Experience with SQL, Spark, and scripting languages such as Python for data processing and model development.
Expertise in cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes.
Proven experience in deploying machine learning systems in a production environment, ensuring scalability, reliability, and high availability.
Extensive experience with object-oriented design (OOD), design patterns, and writing clean, maintainable code.
Solid understanding of distributed systems and the challenges associated with scaling machine learning models in production.
Expertise in implementing MLOps practices, including setting up continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for machine learning models.
Strong understanding of system architecture, performance optimization, and the ability to design fault-tolerant systems that handle large-scale data and high-volume requests.
Experience designing, building, and maintaining ETL pipelines, streamlining data collection, transformation, and storage for model development.
Proficient in containerizing applications using Docker and managing deployment and scaling using Kubernetes or similar orchestrators.
Experience setting up monitoring and logging systems for tracking model performance in production environments and ensuring efficient resource utilization.

Nice To Haves

3 years interfacing directly with internal business stakeholders and/or external stakeholders on AIML initiatives
Working experience with cloud provider solutions such as Azure and AWS
Experience utilizing both open source (e.g. llama, Qwen, Mistral) and proprietary (e.g. GPT, Claude) LLMs for appropriate tasks
Experience with tools that power LLM-based AI agents: eval frameworks, agent tooling, RAG pipelines, prompt engineering, etc.
Experience building LLM-based AI agent workflows via both no code/low code and traditional high-code development environments
Experience in ideating, integrating, and designing applications and frontends using React or similar.

Responsibilities

System development: Architect scalable and reliable AIML solutions that align with the company's tech paved path and stakeholder requirements.
Establish ML Best Practice: Develop and implement Software Development Lifecycle (SDLC) best practices for machine learning projects, ensuring scalable, secure, and reliable systems from model development to production deployment. Expected to stay hands-on coding about 70% of the time.
Product Leadership & Feature Backlogs: Define the product roadmap for machine learning solutions and establish feature backlogs. Prioritize key ML features in collaboration with product managers, aligning them with business objectives and technical feasibility.
Optimize Model Performance and Reliability Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments.
End-to-End Model Lifecycle Management: Own the complete lifecycle of ML models, including monitoring, retraining, finetuning and managing versions of models to ensure they continue to meet business needs over time.
Leadership and Mentorship: Guide and mentor machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team.

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.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume