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

• Drive the evolution of the Machine Learning platform and ensure the quality of models • Critically evaluate internal tools and provide recommendations for improvements • Maintain ML model observability, proposing monitoring metrics

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Abnormal AI is looking for a Machine Learning Engineer to join the Message Detection - Attack Detection team. At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. The Attack Detection team plays the central role of building an extremely high recall Detection Engine that can operate on hundreds of millions of messages at milliseconds latency. The team’s mission statement is to provide world-class detector efficacy to tackle the changing attack landscape using a combination of generalizable and auto-trained models as well as specific detectors for high-value attack categories. - Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance. - Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them. - Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. - Work with infrastructure & systems engineers to productionize signals to feed into the detection system. - Write code with testability, readability, edge cases, and errors in mind. - Train models on well-defined datasets to improve model efficacy on specialized attacks. - Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules, and ML modeling. - Analyze FN and FP datasets to categorize capability gaps and recommend short-term feature and rule ideas to improve our detection efficacy. - Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises. Qualifications - 3+ years experience designing, building and deploying machine learning applications in one of the domains of text understanding, entity recognition, NLP experience, computer vision, recommendation systems, or search. - 1+ years of experience with writing stable and production level pipelines for model training and evaluation leading to reproducible models and metrics. - Experience with data analytics and wielding SQL+pandas+spark framework to both build data and metric generation pipelines, and answer critical questions about system efficacy or counterfactual treatments. - Ability to understand business requirements thoroughly and bias toward designing a simplest yet generalizable ML model/system that can accomplish the goal. - Uses a systematic approach to debug both data and system issues within ML/heuristics models. - Fluent with Python and machine learning toolkits like numpy, sklearn, pytorch and tensorflow. - Effective software engineering skills who can find answers quickly from code base and writes structured, readable, well tested and efficient code. - BS degree in Computer Science, Applied Sciences, Information Systems or other related engineering field. Requirements - MS degree in Computer Science, Electrical Engineering or other related engineering field (Nice to Have). - Experience with big data, statistics and Machine Learning (Nice to Have). - Experience with algorithms and optimization (Nice to Have). Benefits - This position is not focused on optimizing existing machine learning models. - This is not a research-oriented role that's two-steps removed from the product or customer. - This is not a statistics/data science meets ML role.