• Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction. • Define metrics, benchmarks, and validation strategies for real-world document workloads. • Design and curate high-quality datasets for supervised training, fine-tuning, and validation. • Create scalable preprocessing pipelines for PDFs, scans, images, forms, and semi-structured documents. • Train and fine-tune transformer-based OCR, VLMs, layout models, and open-source LLMs for document understanding tasks. • Optimize models for reliability, accuracy, and cost efficiency in production environments. • Deploy ML models with modern inference runtimes (vLLM, TGI, TensorRT, ONNX Runtime). • Build guardrails, monitoring, and fallback mechanisms to ensure safe and predictable model behavior. • Develop retrieval and chunking strategies tailored to document structures (tables, forms, multi-page PDFs). • Optimize end-to-end RAG pipelines for semantic search, Q&A, and workflow automation. • Partner with PMs, backend engineers, and product designers to define AI opportunities and translate requirements into technical solutions.

• Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction. • Define metrics, benchmarks, and validation strategies for real-world document workloads. • Design and curate high-quality datasets for supervised training, fine-tuning, and validation. • Create scalable preprocessing pipelines for PDFs, scans, images, forms, and semi-structured documents. • Train and fine-tune transformer-based OCR, VLMs, layout models, and open-source LLMs for document understanding tasks. • Optimize models for reliability, accuracy, and cost efficiency in production environments. • Deploy ML models with modern inference runtimes (vLLM, TGI, TensorRT, ONNX Runtime). • Build guardrails, monitoring, and fallback mechanisms to ensure safe and predictable model behavior. • Develop retrieval and chunking strategies tailored to document structures (tables, forms, multi-page PDFs). • Optimize end-to-end RAG pipelines for semantic search, Q&A, and workflow automation. • Partner with PMs, backend engineers, and product designers to define AI opportunities and translate requirements into technical solutions.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Staff Data Science Engineer, you will be developing, maintaining, testing, and leading projects regarding global search. You will architect a native Search & Discovery Module powered by our Dataverse Stack. Your goal is to deliver a search experience that is fast (<200ms), smart, and personalized. You will transform Search from a utility into a primary driver of gameplay. - Architect Global Search: - Build the foundational architecture for a unified Global Search entry point that creates a "Single Source of Truth" across Players, Teams, and Game Modes. - Optimize for Speed: - Engineer the Prizepicks search stack to achieve <200ms latency targets, evaluating and deciding between Server-side indexing. - Advanced Retrieval Logic: - Implement fuzzy matching, nickname support, and natural language processing (NLP) to handle user queries like "Bron", “lal/LAL/Lakers” or "Rushing Yards" intuitively. - Dataverse Integration: - Partner with Data Engineering to ensure the Search Index is fed by real-time streams of projections, live game states, and social data, ensuring results are never stale. Qualifications - 5+ years of experience in Machine Learning Engineering, with deep expertise in Information Retrieval (IR) and Search technologies. - 3+ years of technical leadership, guiding teams through complex architectural migrations and greenfield builds. - Familiarity with indexing strategies using technologies like Elasticsearch, OpenSearch, Solr, or Vector Databases (Pinecone, Milvus). - A track record of optimizing API response times and database queries for high-throughput, low-latency applications. - Experience with GCP (Kubernetes, Cloud Functions) and Infrastructure as Code (Terraform). Requirements - Experience building "Instant Search" or "Type-ahead" features for high-traffic consumer mobile apps. - Knowledge of mobile-side database technologies (e.g., Realm, SQLite, WatermelonDB) for offline-first or hybrid search architectures. - Experience integrating GenAI or LLMs to power conversational search interfaces. Benefits - Company-subsidized medical, dental, & vision plans - 401(k) plan with company match - Annual bonus - Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!) - Generous paid leave programs, including 16-week paid parental leave and disability benefits - Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked - Company-wide in-person events and team outings - Lifestyle enhancement program - Company equipment provided (Windows & Mac options) - Annual performance reviews with opportunities for growth and career development

• Improve our existing Ads Recommendation systems using your expertise • Identify new opportunities to apply Machine Learning to different parts of the Quora Ads Product • Work with other engineers to implement algorithms and systems in an efficient way • Take end-to-end ownership of Machine Learning systems -- from data pipelines and training, to real-time prediction engine