Data Science @SportsAI:
At Dream Sports AI, we are pushing the boundaries of what is possible in sports experience. We are the team behind Rushline, a product where the game loop is driven by intelligent systems rather than static scripts. We are looking for builders who want to work at the bleeding edge of Generative AI, Large Language Models (LLMs), and Machine Learning. We have successfully laid the groundwork for Rushline’s intelligent core. This includes building robust pipelines for rich sports data, deploying predictive models, and publishing research at top-tier conferences such as AAAI that validates our approach to complex game theory and optimization.
We are now hiring to tackle the complex challenges of the next evolution of Rushline:
- Next-Gen Simulation: moving from basic forecasting to complex, state-aware agent behaviors.
- Generative Immersion: Utilizing LLMs and GenAI to create hyper-personalized game narratives and visual assets on the fly.
- Scale & Strategy: Optimizing inference for real-time decision-making in high-concurrency environments
Your Role:
- Build and ship production ML/AI systems: data preparation, feature pipelines, model training, deployment, monitoring, and iterative upgrades
- AI engineering with LLMs: design, fine-tune, evaluate, and productionize LLM-based solutions (e.g., retrieval-augmented generation, assistants, copilots, content understanding, classification)
- Develop and maintain ML services with strong engineering fundamentals: scalable APIs, proactive monitoring, alerting, rollback strategies, and operational excellence
- Architect reliable ML workflows: reproducible training, model versioning/registry, CI/CD for ML, and guardrails for safe deployment
- Break down complex initiatives into milestones with clear stakeholder visibility and delivery rigor
- Mentor team members and raise the bar on ML and engineering best practices
Qualifiers:
- 3-6 years of experience building, deploying, and maintaining ML solutions in production
- Strong proficiency in Python and SQL
- Deep hands-on experience with PyTorch or TensorFlow
- Experience with distributed data/compute frameworks (Spark / Ray / Dask)
- Strong foundation in machine learning, probability, statistics, and deep learning
- Experience designing end-to-end ML systems at scale (data → training → serving → monitoring)