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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** SeatGeek, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $145,000.0 - $209,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Airflow, Amazon Web Services, C Sharp (Programming Language), Computer Programming, Data Stores, Cursor (Graphical User Interface Elements), Elasticsearch, Fraud Prevention and Detection, Github, Python (Programming Language), PostgreSQL, Machine Learning, Tensorflow, Software Engineering, Datadog, .NET Core, Pytorch, Reliability of Systems, Gitlab, Fastapi, Build Management, Containerization, Scikit Learn, Performance Monitor, Machine Learning Operations, Software Version Control, Golang - **Published:** May 28, 2026 - **Apply:** https://www.dice.com/job-detail/d06e443c-8efd-490b-9cab-368da00316d7 ## About the Role * Experience building and deploying machine learning systems in production environments. We'll be interested in hearing about the systems you've built, the scale you've operated at, and the business impact you've driven * 4+ years of experience in software engineering with at least 2+ years focused on machine learning systems and MLOps * Strong programming skills in Python and experience with ML frameworks like scikit-learn, TensorFlow, PyTorch, or similar * Experience with cloud platforms and containerization technologies * Understanding of both batch and real-time ML systems, including experience with model serving, A/B testing, and performance monitoring * Passion for software craftsmanship and product. You have well-considered opinions about how systems should be built, and hold yourself and your code to a high standard * A product mindset. You think beyond the model accuracy, about user experience, business impact, system reliability, and what makes a great product tick * Commitment to your teammates. You enjoy working with a diverse group of people with different experiences and take pride in mentoring and learning from others Our stack You do not need experience with all of these, but we thought you might be curious. What we care about is your experience, skills, and approach to problem solving. Tools can be learned. * Languages + Frameworks: Python + FastAPI, Go, C# + .NET Core * Datastores: Postgres, MemcachedRedis, Elasticsearch * Cloud: AWS (SageMaker, Redshift, ECS), Airflow for orchestration * Version control: Gitlab * AI Tooling: Cursor, Github Copliot, Claude Code * Observability: Datadog ## Description You will join a group that bridges the gap between research and production-ready ML systems. Your work will directly impact how millions of fans discover and purchase tickets, how we optimize pricing and inventory, how we personalize the SeatGeek experience, and how we prevent fraud across our marketplace. You will design and build ML infrastructure and services that operate at scale, turning complex algorithms into reliable, fast, and maintainable systems that drive business value. What you'll do * Design, build, and deploy machine learning models and systems that operate reliably at scale in production * Build and maintain ML infrastructure including feature stores, model serving platforms, and real-time inference pipelines * Embed on a product engineering team and collaborate closely with data scientists, PMs ,and Software Engineers to translate research and experimental models into production-ready systems * Solve complex technical challenges unique 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 team and discipline boundaries to evangelize ML capabilities and build them into SeatGeek's core product offerings ## Related Videos - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [WeAreDevelopers LIVE - Modern DevOps for IoT Devices and More](https://www.wearedevelopers.com/videos/1805-wearedevelopers-live-modern-devops-for-iot-devices-and-more) - [Go with the Flow: Stop the Leaks Before Your Memory's a Waterfall!](https://www.wearedevelopers.com/videos/100073-go-with-the-flow-stop-the-leaks-before-your-memory-s-a-waterfall) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 119 - ❤️ === ❤️](https://www.wearedevelopers.com/magazine/454-dev-digest-119)