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.
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).
We enjoy an ‘office-first’ culture and maximize opportunities to collaborate, connect and learn together. Our hybrid working models differ depending on your role and location. Occasional travel may be required.
As well as a competitive salary and range of benefits, we’re committed to supporting employee wellbeing and helping you grow your skills, experience and career.
We strive to create an inclusive working environment, where everyone feels a sense of belonging and the ability to make a difference.
Let us know when you apply if you need any assistance during the recruiting process due to a disability.