Senior Machine Learning Engineer, SeatGeek
$145-200k
+ Equity stake
Ticketing platform for live events
Open for applications
Ticketing platform for live events
501-1000 employees
Open for applications
$145-200k
+ Equity stake
501-1000 employees
Company mission
SeatGeek’s mission is to help the world to experience more live.
Role
Who you are
- 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
What the job involves
- 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
- 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
- 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
Our take
For many fans, buying tickets online can be a time-consuming and frustrating process. SeatGeek is setting out to ease this problem by powering an open entertainment industry that provides fans with greater access to experiences, while enabling teams, venues, and shows to have seamless access to their audiences.
SeatGeek launched with a laser-sharp focus on creating a customer-oriented ticketing marketplace for sports and live events. Its commitment to optimizing fan experience has helped distinguish the company from rivals, such as Ticketmaster and Live Nation, with its expanding suite of simple features that have made it the highest-rated and most trusted ticketer.
SeatGeek has secured integration partnerships with Facebook, Spotify and Snapchat, and has an impressive client list that includes the Dallas Cowboys, Manchester City FC, and the Brooklyn Nets. It continues to onboard enterprise clients, expand its international presence and release new products such as "Next Fan Up", positioning it well for future success.
Steph
Company Specialist at Welcome to the Jungle
Insights
21% employee growth in 12 months
Company
Funding (last 2 of 10 rounds)
Aug 2022
$238m
SERIES E
Apr 2017
$76.3m
SERIES D
Total funding: $417.4m
Company benefits
- Health Insurance Coverage
- Ample Paid Time Off
- 401k Matching Program
- Parental Leave - 16 weeks of leave in a 12-month period
- Health and wellness subscriptions to services like One Medical, Ginger.io and Headspace
- Regular virtual team hangs & happy hours + Company-Wide Hackathons 3-4 times per Year
- SeatGeek Monthly Ticket-Purchase Stipend
- Flexible work environment - Work as many days a week in the office as you'd like, or 100% remotely
- WFH Office Setup Stipend
Company HQ
Flatiron District, New York, NY
Leadership
Jack Groetzinger
(Co-Founder & CEO)Worked as a consultant for Bain. Founded and sold another startup, Scribnia, before founding SeatGeek.
Russell D'Souza
(Co-Founder)Worked as an analyst for Monitor. Founded and sold another startup, Scribnia, before founding SeatGeek.
Eric Waller
(Co-Founder)Worked as a Web Developer at Tickerhound. Co-founded and was a Web Developer at Niqos.
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