Machine Learning Engineer II, Document & Vision Intelligence
- New York, United States
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Why Join GEICO?
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 on relentless innovation to exceed our customers' expectations while making a real impact on local communities nationwide.
Founded in 1936, GEICO is a member of the Berkshire Hathaway family of companies and one of the largest auto insurers in the United States. 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.
Role Overview
The vision of the Documents and Vision Intelligence team is to build a unified intelligence layer that transforms unstructured information — both text-based documents and image-based content—into trusted signals that enable downstream automation and decision-making across multiple lines of business.
As a Machine Learning Engineer II, you will serve as a technical lead through the design, development, and deployment of advanced machine learning solutions across the business. This role focuses on building scalable ML systems, applying AI-native thinking to accelerate experimentation and delivery, and partnering closely with product and business stakeholders to solve high-impact problems.
You will be a technical leader for a team of Machine Learning engineers and/or data scientists focused on ensuring ML solutions are robust, high-performing, and seamlessly integrated into production systems. This position requires hands-on engineering strength, strong communication, product and business acumen, and the ability to thrive in ambiguous environments.
Key Responsibilities
Design and implement machine learning models, services, and components that solve real-world business problems in close collaboration with product and business teams.
Write production-grade code for ML models as services and APIs.
Collaborate with cross-functional teams, including product, data engineering, and software development, to integrate machine learning solutions into production systems.
Build and maintain scalable data processing workflows and model deployment infrastructure.
Debug and resolve model performance issues, track relevant metrics, and implement continuous improvements to ensure model accuracy and reliability.
Stay current with modern ML, generative AI, LLM, agentic workflow, and AI engineering tooling, and apply AI-native practices to improve engineering velocity and solution quality.
Lead the design and implementation of complex machine learning solutions across various business units, balancing technical feasibility, product goals, and measurable business impact.
Architect and develop scalable infrastructure for automated model training, hyperparameter tuning, and deployment.
Mentor and guide junior engineers, collaborating closely with machine learning engineers and cross-functional partners to optimize, refine, and operationalize ML solutions.
Own the end-to-end systems for model monitoring, maintenance, and retraining to ensure high availability and performance.
Minimum Qualifications
B.S. in computer science, computer engineering, electrical engineering, machine learning, statistics, mathematics, or a related quantitative field; M.S. or equivalent work experience preferred.
6+ years of experience applying machine learning techniques such as ensemble learning, deep learning, reinforcement learning, NLP, generative AI, or related approaches.
Direct experience designing, building, evaluating, and deploying production-grade ML systems, including model experimentation, evaluation, monitoring, and continuous improvement.
6+ years of experience with SQL, Spark or equivalent distributed data processing tools, Python, and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
4+ years of experience working with cloud platforms and environments such as AWS, Microsoft Azure, Databricks and/or Snowflake, and Kubernetes.
4+ years of experience applying machine learning techniques in a production environment for business solutions.
Demonstrated ability to communicate technical tradeoffs clearly, partner with product and business stakeholders, and operate effectively in ambiguous problem spaces.
Required Skills and Knowledge
Machine Learning, AI Engineering, and Statistical Modeling
Strong foundation in advanced machine learning algorithms, including supervised and unsupervised learning techniques, deep learning, generative AI, and modern AI engineering practices.
Proficiency in statistical modeling, including probability theory and hypothesis testing, to interrogate, analyze, and interpret data effectively.
Programming, MLOps, and Cloud Platforms
Strong programming skills, including proficiency in Python and experience with machine learning frameworks such as TensorFlow, Keras, and PyTorch.
Familiarity with software development best practices, including CI/CD pipelines, containerization such as Docker, and orchestration such as Kubernetes.
Deep understanding of MLOps practices, including model versioning, A/B testing, and continuous deployment.
Deep understanding of cloud computing platforms such as Azure, AWS, or GCP, distributed systems, and large-scale data processing technologies such as Spark and Kafka.
Leadership, Communication, and Analytical Skills
Proven experience leading machine learning projects, managing stakeholders, and scaling ML solutions in production environments.
Excellent communication skills, with the ability to present complex technical topics to both technical and non-technical audiences.
Exceptional problem-solving and analytical skills with a focus on practical, business-oriented outcomes.
Strong product and business acumen, with the ability to translate ambiguous business needs into clear technical direction, phased execution plans, and measurable outcomes.
AI-native mindset, with a demonstrated ability to leverage LLMs, agents, and modern AI tooling as force multipliers to accelerate experimentation, delivery, and decision-making.
Annual Salary
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: Protecting customers through life’s twists and turns with innovation and integrity.
Great Careers:Personalized development programs, mentorship, and certification assistance.
Great Culture:Inclusive and collaborative culture rooted in shared success.
Great Rewards:Competitive pay, benefits, and flexibility to support your well-being and future.
The equal employment opportunity policy of the GEICO Companies provides for a fair and equal employment opportunity for all associates and job applicants regardless of race, color, religious creed, national origin, ancestry, age, gender, pregnancy, sexual orientation, gender identity, marital status, familial status, disability or genetic information, in compliance with applicable federal, state and local law. GEICO hires and promotes individuals solely on the basis of their qualifications for the job to be filled.
GEICO reasonably accommodates qualified individuals with disabilities to enable them to receive equal employment opportunity and/or perform the essential functions of the job, unless the accommodation would impose an undue hardship to the Company. This applies to all applicants and associates. GEICO also provides a work environment in which each associate is able to be productive and work to the best of their ability. We do not condone or tolerate an atmosphere of intimidation or harassment. We expect and require the cooperation of all associates in maintaining an atmosphere free from discrimination and harassment with mutual respect by and for all associates and applicants.