Senior Machine Learning Engineer

SeatGeek is a technology innovator on a mission to disrupt the $300 billion ticketing industry. We have the product, vision, and team to make life better for performers, venues, and fans, and build a generational consumer brand in the process. All we’re missing is you.

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

What you have

• 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

Perks

• Equity stake
• Discretionary annual bonus
• Flexible work environment, allowing you to work as many days a week in the office as you’d like or 100% remotely
• A WFH stipend to support your home office setup
• Unlimited PTO
• Up to 16 weeks of fully-paid family leave
• 401(k) matching
• Student loan matching program
• Health, vision, dental, and life insurance
• Up to $25k towards family building, reproductive health services and Gender-affirming care
• $500 per year for wellness expenses
• Subscriptions to Headspace (meditation), Headspace Care (therapy), and One Medical
• $360 per quarter to spend on tickets to live events
• Annual subscription to Spotify, Apple Music, or Amazon music

The salary range for this role is $145,000 - $209,000 USD. This role is equity eligible. In addition, you may receive a discretionary annual bonus based on individual and company performance. Actual compensation packages within that range are based on a wide array of factors unique to each candidate, including but not limited to skill set, years and depth of experience, certifications, and specific location.