About this Role

Fanatics Betting and Gaming is seeking an Applied Scientist on our cross-functional Applied AI team that is on a mission to 10x FBG with AI. You’ll be Fanatics’ in-house authority on evaluating and improving the performance, safety, and reliability of large language models and other advanced AI systems, partnering closely with engineers, data scientists, and business leaders to turn cutting-edge research into scalable, production-ready solutions.

Responsibilities:

Own LLM & NLP evaluation strategy — define success metrics, curate datasets, and implement scalable evaluation pipelines (e.g., ELO ratings, task completion, bias & robustness suites).Prototype & compare models — rapidly benchmark open‑source and proprietary models, identify trade‑offs in latency, cost, and quality, and recommend the right model for each use case.Advance our tooling — build internal libraries, experiment trackers, and dashboards that enable repeatable, transparent model assessment.Collaborate on model optimization — work with ML engineers to fine‑tune, distill, and accelerate models for production workloads.Publish & evangelize insights — share findings through tech talks, design docs, and peer‑reviewed artifacts; influence architectural decisions across product teams.Contribute to open‑source & standards — represent Fanatics in the broader AI community, submitting pull requests and whitepapers when appropriate.

Skills & Qualifications:

MS/PhD in Computer Science, Machine Learning, or related field—or BS with 5+ years of comparable experienceDeep understanding of NLP, LLM architectures (Transformers, Mixture of Experts), evaluation methodologies, and statistical analysisThrives in ambiguous, green-field settings—comfortable wearing multiple hats and shipping quicklyProficient in Python, with experience building distributed training or inference systems (e.g., Tensor Parallel, DeepSpeed, Ray)Demonstrated ability to move beyond evaluation and implement model improvements in production codeFamiliarity with MLOps (CI/CD for models, model registries, feature stores) and cloud platforms (AWS/GCP/Azure)Excellent written and verbal communication skills; able to simplify complex technical concepts for non‑experts