GEICO
Software Engineer Machine Learning
GEICO AI platform and Infrastructure team is looking for a Software Engineer responsible for designing, building, and maintaining Machine Learning platform to support data science modelling initiatives. This responsibility is exciting and opens the opportunity to support Machine Learning Development Lifecycle (MDLC) at GEICO. We are looking for a highly motivated individual with the ability to collaborate with cross-functional teams to ensure seamless integration of various inter-related systems and hybrid (on-prem and/or cloud) technologies. The candidate must have excellent verbal and written communication skills with a proven ability to work independently and in a team environment. Job Responsibilities Scope, design, and build systems with high scalability, reliability, and resilience Support platform initiatives geared toward model deployment, serving, inferencing, and/or monitoring solutions Research and implement variety of cloud and/or open-source tools and services across the Model development life cycle ranging from IaC (Infrastructure as code) to self-hosted infrastructure implementation. Engage with partner teams to debug production issues, pipeline failures, and system latencies Engage in cross-functional collaboration with teams of developers, data scientists, product managers, network and security, and other areas throughout the entire MDLC lifecycle Lead in design sessions and code reviews with peers Collaborate with Engineering teammates to deploy models into production Collaborate with regulatory team to develop intended use and regulatory strategy Participate in product discussions and roadmap exercises to understand business use cases Author technical documentation and reports to communicate process and results Candidate Qualifications and Skills Technical Skills Programming: Proficiency in Python for data processing, automation, and ML workflows Database: Strong SQL skills for data querying, manipulation, and pipeline development MLOps Tools: Hands-on experience with MLOps platforms such as: Experience with machine learning model serving frameworks and machine learning platforms such as Jupyter notebooks, MLFlow, Azure Machine Learning, Large Language Model serving and optimizations etc. Experience with MLOps practices such as model versioning, model monitoring, and model governance Docker for containerization Azure ML, AWS SageMaker, or Google Cloud AI Platform Alternative cloud-based ML solutions Version Control & CI/CD Proficiency with Git for version control Experience with GitHub Actions or similar CI/CD tools Understanding of automated testing and deployment processes Core Competencies Understanding of machine learning model lifecycle and deployment challenges Experience with cloud computing platforms (AWS, Azure, or GCP) Knowledge of software engineering best practices and code quality standards Strong problem-solving skills and attention to detail Excellent communication skills for cross-functional collaboration Preferred Qualifications Experience with Kubernetes for container orchestration Familiarity with infrastructure as code tools (Terraform, CloudFormation) Knowledge of data workflow orchestration tools (Airflow, Prefect, Argo) Experience with monitoring and observability tools (Prometheus, Grafana) Understanding of machine learning frameworks (PyTorch, TensorFlow, scikit-learn) Experience with stream processing technologies (Kafka, Kinesis) Knowledge of data security and compliance requirements Working knowledge of networking concepts (DNS/DHCP/Firewalls/Sub-netting, etc.) Knowledge of Big Data platforms such as Snowflake, ADLS, Databricks, Cosmos DB Knowledge of Big Data processing frameworks and languages such as Spark, Scala Experience with at least one IaC (Infrastructure as code) provider, preferably Terraform Experience with implementing monitoring and alerting systems to ensure performance and reliability of deployed models Experience with infrastructure optimization for cost efficiency, scalability, and reliability Knowledge of microservice architecture and distributed systems Experience performing Root Cause Analysis (RCA) for application and infrastructure related issues Education & Experience Education : Bachelor's degree in computer science, Engineering, Data Science, or related technical field Experience : Level 1: 0-2 years of relevant experience in software engineering, data engineering, or ML infrastructure Level 2: 2-4 years of relevant experience with demonstrated MLOps or platform engineering experience Internships, personal projects, or bootcamp experience in ML/data engineering will be considered What We Offer Opportunity to work on cutting-edge ML infrastructure and tools Mentorship from senior engineers and data scientists Professional development budget for conferences, courses, and certifications Collaborative environment with cross-functional ML teams Competitive salary and benefits package Growth Opportunities Lead infrastructure projects and mentor junior team members Specialize in advanced MLOps technologies and practices Contribute to open-source ML tools and frameworks Present at conferences and contribute to the ML engineering community Location Remote Annual Salary $75,000.00 - $160,000.00 The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate's work experience, education and training, the work location as well as market and business considerations. GEICO will consider sponsoring a new qualified applicant for employment authorization for this position. The GEICO Pledge: Great Company:
At GEICO, we help our customers through life's twists and turns. Our mission is to protect people when they need it most and we're constantly evolving to stay ahead of their needs. We're an iconic brand that thrives on innovation, exceeding our customers' expectations and enabling our collective success. From day one, you'll take on exciting challenges that help you grow and collaborate with dynamic teams who want to make a positive impact on people's lives. Great Careers:
We offer a career where you can learn, grow, and thrive through personalized development programs, created with your career and your potential in mind. You'll have access to industry leading training, certification assistance, career mentorship and coaching with supportive leaders at all levels. Great Culture:
We foster an inclusive culture of shared success, rooted in integrity, a bias for action and a winning mindset. Grounded by our core values, we have an established culture of caring, inclusion, and belonging, that values different perspectives. Our teams are led by dynamic, multi-faceted teams led by supportive leaders, driven by performance excellence and unified under a shared purpose. As part of our culture, we also offer employee engagement and recognition programs that reward the positive impact our work makes on the lives of our customers. 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.
GEICO AI platform and Infrastructure team is looking for a Software Engineer responsible for designing, building, and maintaining Machine Learning platform to support data science modelling initiatives. This responsibility is exciting and opens the opportunity to support Machine Learning Development Lifecycle (MDLC) at GEICO. We are looking for a highly motivated individual with the ability to collaborate with cross-functional teams to ensure seamless integration of various inter-related systems and hybrid (on-prem and/or cloud) technologies. The candidate must have excellent verbal and written communication skills with a proven ability to work independently and in a team environment. Job Responsibilities Scope, design, and build systems with high scalability, reliability, and resilience Support platform initiatives geared toward model deployment, serving, inferencing, and/or monitoring solutions Research and implement variety of cloud and/or open-source tools and services across the Model development life cycle ranging from IaC (Infrastructure as code) to self-hosted infrastructure implementation. Engage with partner teams to debug production issues, pipeline failures, and system latencies Engage in cross-functional collaboration with teams of developers, data scientists, product managers, network and security, and other areas throughout the entire MDLC lifecycle Lead in design sessions and code reviews with peers Collaborate with Engineering teammates to deploy models into production Collaborate with regulatory team to develop intended use and regulatory strategy Participate in product discussions and roadmap exercises to understand business use cases Author technical documentation and reports to communicate process and results Candidate Qualifications and Skills Technical Skills Programming: Proficiency in Python for data processing, automation, and ML workflows Database: Strong SQL skills for data querying, manipulation, and pipeline development MLOps Tools: Hands-on experience with MLOps platforms such as: Experience with machine learning model serving frameworks and machine learning platforms such as Jupyter notebooks, MLFlow, Azure Machine Learning, Large Language Model serving and optimizations etc. Experience with MLOps practices such as model versioning, model monitoring, and model governance Docker for containerization Azure ML, AWS SageMaker, or Google Cloud AI Platform Alternative cloud-based ML solutions Version Control & CI/CD Proficiency with Git for version control Experience with GitHub Actions or similar CI/CD tools Understanding of automated testing and deployment processes Core Competencies Understanding of machine learning model lifecycle and deployment challenges Experience with cloud computing platforms (AWS, Azure, or GCP) Knowledge of software engineering best practices and code quality standards Strong problem-solving skills and attention to detail Excellent communication skills for cross-functional collaboration Preferred Qualifications Experience with Kubernetes for container orchestration Familiarity with infrastructure as code tools (Terraform, CloudFormation) Knowledge of data workflow orchestration tools (Airflow, Prefect, Argo) Experience with monitoring and observability tools (Prometheus, Grafana) Understanding of machine learning frameworks (PyTorch, TensorFlow, scikit-learn) Experience with stream processing technologies (Kafka, Kinesis) Knowledge of data security and compliance requirements Working knowledge of networking concepts (DNS/DHCP/Firewalls/Sub-netting, etc.) Knowledge of Big Data platforms such as Snowflake, ADLS, Databricks, Cosmos DB Knowledge of Big Data processing frameworks and languages such as Spark, Scala Experience with at least one IaC (Infrastructure as code) provider, preferably Terraform Experience with implementing monitoring and alerting systems to ensure performance and reliability of deployed models Experience with infrastructure optimization for cost efficiency, scalability, and reliability Knowledge of microservice architecture and distributed systems Experience performing Root Cause Analysis (RCA) for application and infrastructure related issues Education & Experience Education : Bachelor's degree in computer science, Engineering, Data Science, or related technical field Experience : Level 1: 0-2 years of relevant experience in software engineering, data engineering, or ML infrastructure Level 2: 2-4 years of relevant experience with demonstrated MLOps or platform engineering experience Internships, personal projects, or bootcamp experience in ML/data engineering will be considered What We Offer Opportunity to work on cutting-edge ML infrastructure and tools Mentorship from senior engineers and data scientists Professional development budget for conferences, courses, and certifications Collaborative environment with cross-functional ML teams Competitive salary and benefits package Growth Opportunities Lead infrastructure projects and mentor junior team members Specialize in advanced MLOps technologies and practices Contribute to open-source ML tools and frameworks Present at conferences and contribute to the ML engineering community Location Remote Annual Salary $75,000.00 - $160,000.00 The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate's work experience, education and training, the work location as well as market and business considerations. GEICO will consider sponsoring a new qualified applicant for employment authorization for this position. The GEICO Pledge: Great Company:
At GEICO, we help our customers through life's twists and turns. Our mission is to protect people when they need it most and we're constantly evolving to stay ahead of their needs. We're an iconic brand that thrives on innovation, exceeding our customers' expectations and enabling our collective success. From day one, you'll take on exciting challenges that help you grow and collaborate with dynamic teams who want to make a positive impact on people's lives. Great Careers:
We offer a career where you can learn, grow, and thrive through personalized development programs, created with your career and your potential in mind. You'll have access to industry leading training, certification assistance, career mentorship and coaching with supportive leaders at all levels. Great Culture:
We foster an inclusive culture of shared success, rooted in integrity, a bias for action and a winning mindset. Grounded by our core values, we have an established culture of caring, inclusion, and belonging, that values different perspectives. Our teams are led by dynamic, multi-faceted teams led by supportive leaders, driven by performance excellence and unified under a shared purpose. As part of our culture, we also offer employee engagement and recognition programs that reward the positive impact our work makes on the lives of our customers. 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.