Senior Applied AI Engineer

Genius SportsLausanne, Vaud, Switzerland

On-site Full-time

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Experience Level

Senior

Qualifications

The ideal candidate will have extensive experience in AI engineering, with a focus on multimodal systems. Proficiency in machine learning frameworks, data processing, and software development practices is essential. Strong problem-solving skills, effective communication, and a collaborative mindset are crucial for success in this role. Familiarity with Agile methodologies and a commitment to continuous improvement are also important.

About the job

About the Role

We are seeking a skilled Senior Applied AI Engineer to develop production-grade, multimodal systems (audio/video/text) that transform broadcast and radio feeds into structured, real-time signals and event candidates. You will play a pivotal role in implementing and enhancing “agentic” components, such as sensor agents and decision logic, which power innovative products like Audio Intelligence, semi-automated broadcast-to-data tagging, and highlight/momentum signals.

Your technical expertise and pragmatic problem-solving approach will be essential as you collaborate within a team that values Agile delivery principles and continuous improvement. You will adopt a data-driven mindset, embracing the principles of continuous experimentation and validation.

Key Responsibilities

Develop and maintain multimodal agents:

Audio sensor agents (acoustic events, sentiment analysis, alignment)
Visual sensor agents (scorebug/overlay interpretation, basic visual cues)
Specialist and decision logic components (structured event outputs, confidence scoring, traceability)

Implement streaming-friendly pipelines encompassing chunking, normalization, time synchronization, asynchronous execution, and robust retry/backoff strategies for model/tool calls.
Develop prompt-as-code with strict JSON contracts, schema validation, and deterministic post-processing to enhance system stability.
Enhance system resilience against noisy inputs by:

Designing fallback behaviors for degraded modes
Incorporating guardrails and confidence thresholds
Instrumenting traces and metrics for latency, cost, and accuracy

Collaborate with product, platform, and domain leads to translate sports rules and edge cases into validation logic and integrate outputs into downstream consumers (tagging, live feeds, analytics).
Contribute to the evaluation workflow by adding test cases, failure mode categories, and regression checks for prompts and model routing.
Stay updated with emerging generative AI technologies, tools, and trends.

About Genius Sports

Genius Sports is a pioneering technology company transforming the sports industry by delivering innovative data-driven solutions that enhance the fan experience. Our commitment to excellence and continuous evolution sets us apart in the competitive landscape of sports technology.

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Full-time|On-site|Lausanne

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Jan 26, 2026

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Senior Applied AI Engineer

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Full-time|On-site|Lausanne, Vaud, Switzerland

At Genius Sports, we’re revolutionizing the sports experience for fans around the globe by leveraging cutting-edge technology and unparalleled live data, delivering immersive, interactive, and personalized experiences like never before. Discover more at geniussports.com.About the Role We are seeking a skilled Senior Applied AI Engineer to develop production-grade, multimodal systems (audio/video/text) that transform broadcast and radio feeds into structured, real-time signals and event candidates. You will play a pivotal role in implementing and enhancing “agentic” components, such as sensor agents and decision logic, which power innovative products like Audio Intelligence, semi-automated broadcast-to-data tagging, and highlight/momentum signals.Your technical expertise and pragmatic problem-solving approach will be essential as you collaborate within a team that values Agile delivery principles and continuous improvement. You will adopt a data-driven mindset, embracing the principles of continuous experimentation and validation.Key Responsibilities Develop and maintain multimodal agents:Audio sensor agents (acoustic events, sentiment analysis, alignment)Visual sensor agents (scorebug/overlay interpretation, basic visual cues)Specialist and decision logic components (structured event outputs, confidence scoring, traceability)Implement streaming-friendly pipelines encompassing chunking, normalization, time synchronization, asynchronous execution, and robust retry/backoff strategies for model/tool calls.Develop prompt-as-code with strict JSON contracts, schema validation, and deterministic post-processing to enhance system stability.Enhance system resilience against noisy inputs by:Designing fallback behaviors for degraded modesIncorporating guardrails and confidence thresholdsInstrumenting traces and metrics for latency, cost, and accuracyCollaborate with product, platform, and domain leads to translate sports rules and edge cases into validation logic and integrate outputs into downstream consumers (tagging, live feeds, analytics).Contribute to the evaluation workflow by adding test cases, failure mode categories, and regression checks for prompts and model routing.Stay updated with emerging generative AI technologies, tools, and trends.

Mar 5, 2026

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