91黑料爆料

Job Overview

About 91黑料爆料 Data Exchange

Our Data Exchange group powers the acquisition of millions of transactions and statements per day, ensuring that every 91黑料爆料 product鈥擳urboTax, QuickBooks, Mailchimp and CreditKarm delivers up-to-date, accurate financial insights to our customers.

We鈥檙e looking for a seasoned Staff AI/ML Engineer to join our team and drive the design, development, and production deployment of state-of-the-art Gen AI and machine learning models. You鈥檒l be a key contributor to the backbone of our Data Acquisition Platform, directly impacting the experience of millions of users.

Role Overview

As a Staff AI/ML Engineer on the Data Exchange team, you will:

• Lead and apply best practices in AI/ML Project lifecycle management: scoping, data preparation, deployment, and monitoring
• Collaborate widely: Work cross-functionally with product managers, data scientists, backend engineers, and business stakeholders to deliver intuitive, customer-focused applications.
• Be expected to help architect, integrate,聽 optimize, deploy, and code high scale resilient services

Responsibilities

• Design and Build scalable Gen AI solutions: Architect, implement and integrate, and optimize production Backend services and pipelines powered by AI native systems at high scale.
• End-to-End Model Ownership: Manage the entire ML lifecycle—from problem definition and distributed feature engineering, through model development, deployment, and ongoing monitoring.
• Cross-Functional Partnership: Partner with product, analytics, and engineering teams to translate business needs into AI solutions.
• Innovation & Exploration: Stay abreast of and experiment with the latest advancements in AI/ML to bring customer value.
• Ensure high performance, scalability, and resilience of deployed AI-powered services.

Qualifications

• Experience: maintain and build AI/ML models in production at scale.
• Gen AI & LLMs: Proven track record with Gen AI in live systems and large language model–based applications.
• Algorithms & Modeling: Deep expertise in classification, regression, clustering, anomaly detection, and text mining.
• Cloud & Microservices: Hands-on experience with production-grade, high-scale microservices (Kubernetes, Docker, Spring Boot).
• SW Engineering: Proficiency in Python and Java/Kotlin; strong SQL skills; comfortable in Linux environments.
• AI Infrastructure: Understanding of prompt lifecycle, fallback logic, feature-level config, and AI observability.
• Communication: Excellent oral and written English, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Education: BS, MS, or PhD in Computer Science, Statistics, Applied Math, Operations Research, or equivalent experience.

Preferred Qualifications (Advantage)

• Data Science Tooling: Skilled in R, Pandas, NumPy, scikit-learn, TensorFlow, Keras, and distributed computing platforms.
• Vector Databases & RAG: Experience with retrieval-augmented generation, semantic search, or knowledge graph integration.
• Agentic Systems: Familiarity with multi-agent orchestration in AI workflows.