Senior Applied AI Engineer
About The Position
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.
Requirements
- 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.
Nice To Haves
- 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).
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.
Benefits
- 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.
- One team, being brave, driving change
- We strive to create an inclusive working environment, where everyone feels a sense of belonging and the ability to make a difference.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
