Skip to content
Software Engineer – Sports Data Products
| Company | Genius Sports |
|---|
| Location | Los Angeles, CA, USA |
|---|
| Salary | $125000 – $175000 |
|---|
| Type | Full-Time |
|---|
| Degrees | |
|---|
| Experience Level | Mid Level, Senior |
|---|
Requirements
- Minimum 4 years of industry software engineering experience
- Experience with our languages and technologies in our stack
- Experience building systems that parse and manipulate large datasets
- Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback
- Strong communication skills when discussing technical concepts with technical and non-technical colleagues
- Self-motivation and ability to work independently in an efficient manner
- Flexibility and availability for work on-call, outside of regular work hours, as needed to support live sports
Responsibilities
- Develop, test, and maintain production systems that utilize high-volume, low-latency live player tracking data to empower live decision making.
- Coordinate development with related engineering, product and operational teams
- Develop, document, and communicate processes for operational staff
- Understand both business and sport-related needs for our products and make technical decisions to ensure optimal outcomes from the technology
- Maintain and promote best practices for software development, including deployment process, documentation, and coding standards
- Contribute to technical and product discussions, and share knowledge and ideas with colleagues across the company
Preferred Qualifications
- Experience working with relevant datasets, e.g. skeletal pose data and/or sports player tracking data
- Experience building highly performant software systems using Rust
- Expertise with live and realtime data parsing and processing pipelines
- Experience leading software projects and being responsible for complex production software
- Knowledge of soccer terms, rules, tactics and norms is a plus
- Experience building and working with machine learning models and pipelines is a plus
