We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. Backed by Revolution.
About the role
While we appreciate the flexibility and benefits of working from home, we strongly believe that coming together in person fosters stronger connections, encourages collaboration, and drives innovation—both as individuals and as a company. The energy, shared ideas, and team support we experience in the office strengthen the foundation of our success and culture. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Around 2+ years of software engineering or applied ML experience, or equivalent project experience
- Clear communication with non-technical colleagues, able to explain technical topics simply
- Self-motivation and the ability to work independently with little supervision
- Pragmatic approach to organisational complexity and competing priorities
- Structured approach to finding problems and simplifying complexity
- Solid Python and software engineering fundamentals (testing, version control, APIs)
More about this role
Sportradar is a sports data and technology company. As Forward Deployed AI Engineer, you work like an internal consultant: you are placed in a department or business unit, talk with the people there, find the real problems, cut through complexity, simplify how work is done, and then build and ship LLM and agent-based solutions. You work largely independently with little day-to-day supervision, find your way through organisational complexity and competing priorities to get things done, and learn fast. Senior engineers are available for guidance.
- Talk with colleagues in your department to understand workflows, pain points and data.
- Identify problems worth solving and break unclear or cluttered situations into clear, manageable steps.
- Streamline and simplify processes, and propose where an AI solution makes sense and where it does not.
- Work with several stakeholders and competing priorities, and keep your work moving pragmatically.
- Build, test and deploy LLM-powered applications and agents, from prototype to production.
- Implement retrieval (RAG), tool integrations, prompts and context handling.
- Build and run evaluations (test sets, automated checks, quality dashboards)...
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