This role is for an ML engineer who can take models out of notebooks and make them real, fast, reliable systems in production. You’ll build ML infrastructure and services that power discovery, personalization, pricing and inventory optimization, and fraud prevention across the SeatGeek marketplace.
About SeatGeek
SeatGeek is a live events ticketing platform built with a fan-first mindset and a strong technology backbone. They’re building products that help fans discover and buy tickets while supporting performers, venues, and partners at scale. The mission is to modernize ticketing and build a generational consumer brand.
Schedule
- Remote, United States
- Flexible work environment (in-office as often as you’d like or 100% remote)
What You’ll Do
- Design, build, and deploy machine learning models and production ML systems that operate reliably at scale
- Build and maintain ML infrastructure including feature stores, model serving platforms, and real-time inference pipelines
- Embed with product engineering teams and collaborate with data scientists, PMs, and software engineers to productionize research and experimental models
- Solve complex ticketing-specific problems like real-time pricing optimization, demand forecasting, and fraud detection
- Create automated pipelines for training, validation, deployment, and monitoring using MLOps best practices
- Evangelize ML capabilities across teams and help bake them into SeatGeek’s core product offerings
What You Need
- Strong experience building and deploying machine learning systems in production, including scale and business impact
- 4+ years of software engineering experience, with 2+ years focused on ML systems and MLOps
- Strong Python skills and experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, or similar)
- Experience with cloud platforms and containerization technologies
- Understanding of batch and real-time ML systems, including model serving, A/B testing, and performance monitoring
- High standards for software craftsmanship, maintainability, and system reliability
- Product mindset that considers user experience, business impact, and operational quality beyond model accuracy
- Collaborative approach, commitment to teammates, and interest in mentoring and learning
Benefits
- Equity stake
- Flexible work environment (in-office as often as you’d like or 100% remote)
- Work-from-home stipend for home office setup
- Unlimited PTO
- Up to 16 weeks fully paid family leave
- 401(k) matching program
- Student loan support resources
- Health, vision, dental, and life insurance
- Up to $25k toward family building and reproductive health services
- Gender-affirming care support program
- $500 per year for wellness expenses
- Subscriptions to Headspace, Headspace Care, and One Medical
- $120 per month for live event tickets
- Annual subscription to Spotify, Apple Music, or Amazon Music
If you’re the kind of engineer who gets excited about feature stores, serving infrastructure, monitoring, and making models resilient in the wild, this is a strong fit.
This is not “just build a model” work. It’s build the system that keeps the model alive, measurable, and valuable.
Happy Hunting,
~Two Chicks…