Senior Machine Learning Engineer

About the Company SeatGeek is committed to providing equal employment opportunities to employees and applicants and maintaining a diverse and inclusive workplace free from unlawful discrimination. About the Role SeatGeek is seeking a Senior Machine Learning Engineer to bridge the gap between machine learning research and production-ready ML systems. You will design, build, and operate reliable machine learning infrastructure and services that support how millions of fans discover and purchase tickets, while contributing to pricing optimization, demand forecasting, personalization, and fraud detection. You will work closely with data scientists, product managers, and software engineers to translate experimental models into scalable production systems. The role combines machine learning engineering, MLOps, software engineering, and product thinking, with opportunities to build ML capabilities into SeatGeek's core product offerings. Responsibilities Design, build, and deploy machine learning models and systems that operate reliably at production scale.Build and maintain ML infrastructure, including feature stores, model serving platforms, and real-time inference pipelines.Embed with a product engineering team and collaborate with data scientists, product managers, and software engineers.Translate research and experimental ML models into production-ready systems.Solve technical challenges related to the ticketing industry, including real-time pricing optimization, demand forecasting, and fraud detection.Develop automated ML pipelines for training, validation, deployment, and monitoring using MLOps best practices.Work across teams and disciplines to expand ML capabilities and integrate them into SeatGeek's core products.Design systems with a focus on reliability, maintainability, performance, user experience, and business impact.Contribute to software craftsmanship and maintain high engineering standards.Mentor teammates while continuing to learn from colleagues with diverse experiences. Qualifications Experience building and deploying machine learning systems in production environments.4+ years of software engineering experience, including at least 2+ years focused on machine learning systems and MLOps.Strong programming skills in Python.Experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.Experience with cloud platforms and containerization technologies.Understanding of both batch and real-time machine learning systems.Experience with model serving, A/B testing, and performance monitoring.Strong software engineering and problem-solving skills.Product mindset with an understanding of user experience, business impact, system reliability, and model performance.Ability to collaborate effectively with data scientists, product managers, software engineers, and other teams. Required Skills Strong programming skills in Python.Experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.Experience with cloud platforms and containerization technologies.Experience with model serving, A/B testing, and performance monitoring.Strong software engineering and problem-solving skills. Preferred Skills Experience operating production ML systems at scale.Experience with feature stores, ML infrastructure, model serving, and real-time inference.Experience with MLOps practices and automated ML lifecycle pipelines.Experience with AWS, including SageMaker, Redshift, or ECS.Experience with FastAPI, Go, C#/.NET Core, or similar technologies.Experience with Postgres, Memcached, Redis, or Elasticsearch.Experience with Airflow for orchestration.Experience with GitLab for version control.Familiarity with AI development tools such as Cursor, GitHub Copilot, or Claude Code.Experience with observability platforms such as Datadog. Required Skills Technology StackLanguages & Frameworks: Python, FastAPI, Go, C#, .NET CoreDatastores: Postgres, Memcached, Redis, ElasticsearchCloud: AWS, SageMaker, Redshift, ECSOrchestration: AirflowVersion Control: GitLabAI Tooling: Cursor, GitHub Copilot, Claude CodeObservability: DatadogExperience with every technology in the stack is not required. SeatGeek emphasizes relevant experience, skills, and problem-solving approach, with the understanding that tools can be learned. Pay range and compensation package Salary range: $145,000–$209,000 USDEquity eligibleDiscretionary annual bonus based on individual and company performanceActual compensation within the stated range may vary based on factors including skill set, years and depth of experience, certifications, and specific location. Benefits & Perks Flexible work environment, including the option to work 100% remotelyWFH stipend for home office setupUnlimited PTOUp to 16 weeks of fully paid family leave401(k) matchingStudent loan matching programHealth, vision, dental, and life insuranceUp to $25,000 toward family building, reproductive health services, and gender-affirming care$500 per year for wellness expensesSubscriptions to Headspace, Headspace Care, and One Medical$360 per quarter for live event ticketsAnnual subscription to Spotify, Apple Music, or Amazon Music Work Arrangement RemoteLocation: United States Equal Opportunity Statement