Senior Applied AI Engineer

By bringing together next-gen technology and the finest live data available, Genius Sports is enabling a new era of sports for fans worldwide, delivering experiences that are more immersive, interactive and personalized than ever before. Learn more at geniussports.com. 

About the Role  

We are looking for a Senior Applied AI Engineer to build production-grade, multimodal (audio/video/text) systems that convert broadcast and radio feeds into structured, real-time signals and event candidates. You will implement and evolve “agentic” components (sensor agents, specialist agents, decision logic) that power products like Audio Intelligence, semi-automated broadcast-to-data tagging, and highlight/momentum signals. 

We will lean on your technical expertise and your pragmatic approach to problem solving; working in a team that prioritizes the principles of Agile delivery and continuous improvement. You will have a Data-driven, evidence-based mentality, comfortable with the principles of continuous experimentation and validation.  

Key Responsibilities 

• Build and maintain multimodal agents:
• Audio sensor agents (acoustic events, sentiment, alignment)
• Visual sensor agents (scorebug/overlay reading, basic visual cues when applicable)
• Specialist and decision logic components (structured event outputs, confidence, traceability) 
• Implement streaming-friendly pipelines: chunking, normalization, time-sync, async execution, and robust retry/backoff for model/tool calls. 
• Develop prompt-as-code with strict JSON contracts, schema validation, and deterministic post-processing to reduce brittleness.
• Improve system robustness under noisy inputs by:
• Designing fallback behaviors (degraded modes)
• Adding guardrails and confidence thresholds
• Instrumenting traces/metrics for latency + cost + accuracy
• Partner with product, platform, and domain leads to translate sport rules/edge cases into validation logic and to 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 up-to-date with emerging Gen AI technologies, tools, and best practices.
• Mentor and support other team members in data engineering principles and practices.

  
Qualifications   

• 5–8+ years of professional software engineering experience (backend and/or ML systems).
• Strong proficiency in one or more of: Python, Java, Rust.
• Hands-on experience building production services involving LLM or multimodal model integration (including Gemini, ChatGPT or Claude).
• Comfortable with ambiguity, iterative experimentation, and evidence-based decision-making in an Agile environment.
• Experience with streaming data platforms like Kafka, Pulsar, Flink
• Experience with AWS Bedrock or Google Vertex AI
• Familiarity with version control systems (e.g., Git).
• Excellent problem-solving skills and attention to detail.
• Ability to work independently and as part of a team.
• Strong communication skills.

  
Preferred Qualifications   

• Experience with audio ML / speech / acoustic event detection, or media pipelines (audio/video chunking, sync).
• Experience with RAG or rules/config grounding for sport-specific logic (league configs, terminology, rulebooks).
• Familiarity with evaluation practices (golden sets, precision/recall, drift monitoring) and production observability.
• Experience operating systems where cost/latency tradeoffs matter (routing “flash vs heavy” models, caching, batching).

The salary for this role is based on an annualized range of $180,000 - $230,000 USD. This rol