{"id":2272815,"url":"https://alion.io/job/geico-senior-machine-learning-engineer-fraud-risk-modeling","title":"Senior Machine Learning Engineer, Fraud Risk Modeling","company":{"id":2667051,"name":"GEICO","domain":"geico.com","url":"https://alion.io/company/geico-3","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":34,"ghost_share":0,"stale_share":0.765,"repost_share":0.029,"time_to_fill_p50_days":27,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":115000,"max":230000,"currency":"USD","period":"year","gross":null,"usd_annual":230000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"C++","optional":false},{"name":"Cassandra","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"TPU","optional":true}],"status":"closed","first_seen_at":"2026-10-06T00:00:00Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-20T18:15:28Z","board_verified":false,"closed_at":"2026-10-20T18:15:28Z","days_open":14,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":14},"description":"Why Join GEICO?\nAt GEICO, we offer a rewarding career where your ambitions are met with endless possibilities.\nEvery 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.\nFounded 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.\nGEICO is seeking an experienced Senior Machine Learning Engineer to join the AI organization. This person will take on a critical leadership role in designing, implementing, and deploying cutting-edge machine learning models that solve real-world business challenges. You will collaborate with various business units to build scalable, high-performance ML systems with a strong emphasis on system design. In addition to technical contributions, you will mentor junior engineers, drive the full lifecycle of machine learning model development, and ensure that models are seamlessly integrated into production. This position requires expertise in both machine learning and software engineering to develop robust, production-grade solutions.\nKey Responsibilities:\nLead the Design & Implementation of ML Models: Lead the architecture and implementation of machine learning models, working closely with Product, Business Units, and Engineering teams.\nBuild Scalable Infrastructure: Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines, ensuring that systems are reliable and performant at scale.\nWrite Production-Grade Code for ML Services and APIs: Write high-quality, maintainable production-grade code that turns machine learning models into deployable services and APIs. Ensure that code is modular and reusable for future ML projects.\nOptimize Model Performance and Resolve Issues: Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments.\nEnd-to-End Model Lifecycle Management: Own the complete lifecycle of ML models, including monitoring, retraining, and managing versions of models to ensure they continue to meet business needs over time.\nLeadership and Mentorship: Guide and mentor junior machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team.\nCollaboration Across Teams: Collaborate with cross-functional teams (e.g., data engineering, software development, and product management) to integrate machine learning models and ensure smooth deployment and operations in production systems.\nStay Up to Date with Industry Trends: Continuously explore and integrate new machine learning techniques and system engineering tools, ensuring the team remains at the forefront of machine learning and systems architecture practices.\nBasic Qualifications:\nB.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.\n6+ years of hands-on experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments.\n6+ years of experience utilizing open-source/cloud-agnostic components such as data warehouse (e.g. snowflake), streaming platform (e.g. Kafka), relational database (e.g. PostgreSQL), NoSQL (e.g. MongoDB, Cassandra), distributed processing (e.g. Spark, Ray), workflow management (e.g. Airflow, Temporal), etc.\n6+ years of professional software development experience with at least two general-purpose programming languages such as Java, C++, Python or C#.\n6+ years of experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn for model development.\nAt least 4 years of experience with cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes.\nProven experience in deploying machine learning models in a production environment, ensuring scalability, reliability, and high availability.\nCore Engineering Skills & Knowledge:\nExtensive experience with object-oriented design (OOD), design patterns, writing clean, and maintainable code. Proficiency in version control (Git) and familiarity with Agile methodologies.\nSolid understanding of distributed systems and the challenges associated with scaling machine learning models in production, such as managing distributed data processing and microservices architectures.\nExpertise in implementing MLOPs practices, including setting up continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for ML models.\nStrong understanding of system architecture, performance optimization, designing fault-tolerant systems that handle large-scale data and high-volume requests.\nExperience designing and deploying machine learning models using cloud-based environments like AWS, Azure, or Google Cloud. Familiarity with cloud-native tools such as AWS Sage Maker, GCP AI Platform, or Azure Machine Learning.\nExperience setting up monitoring and logging systems to track performance in production environments and ensuring efficient resource utilization.\nPreferred Qualifications:\nExperience with designing and building high-performance distributed systems that handle large-scale data ingestion and processing for machine learning workloads.\nExperience with real-time inference pipelines and low-latency model serving.\nFamiliar with serverless computing or managed services for ML model deployment.\nAdvanced degree (M.Sc., Ph.D.) in a related field is a plus.\nExperience in working with GPU/TPU optimization for accelerated model training and inference.\nAnnual Salary\n$115,000.00 - $230,000.00The 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.\nGEICO will consider sponsoring a new qualified applicant for employment authorization for this position.The GEICO Pledge:\nGreat Company: Protecting customers through life’s twists and turns with innovation and integrity.\nGreat Careers: Personalized development programs, mentorship, and certification assistance.\nGreat Culture: Inclusive and collaborative culture rooted in shared success.\nGreat Rewards: Competitive pay, benefits, and flexibility to support your well-being and future.\nThe 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.\nGEICO 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.","description_format":"text","description_chars":8174,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Property & Casualty Insurance"],"lifecycle":[{"event":"open","at":"2026-10-11T08:01:41Z"},{"event":"close","at":"2026-10-20T18:15:28Z"}],"visa":[],"liveness":null,"pay":{"stated_usd_annual":230000,"is_top_pay":true},"html_url":"https://alion.io/job/geico-senior-machine-learning-engineer-fraud-risk-modeling","json_url":"https://alion.io/job/geico-senior-machine-learning-engineer-fraud-risk-modeling.json","meta":{"generated_at":"2026-10-21T22:17:41Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":31721,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-22T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2667051},"rest":"https://alion.io/mcp/rest/get_company?id=2667051"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fgeico-senior-machine-learning-engineer-fraud-risk-modeling"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fgeico-senior-machine-learning-engineer-fraud-risk-modeling"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fgeico-senior-machine-learning-engineer-fraud-risk-modeling"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/geico-senior-machine-learning-engineer-fraud-risk-modeling\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fgeico-senior-machine-learning-engineer-fraud-risk-modeling"}]}