Job VC
Data Science for News Service
Technologies
Description
Job Description
Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
8+ years of professional experience in machine learning, data science, or AI engineering.
Proven experience as a technical lead or solution architect for ML/AI projects, with accountability for end-to-end delivery in a production environment.
Strong proficiency in Python and the modern ML/AI ecosystem (e.g., PyTorch, Hugging Face, LangChain/LangGraph, Scikit-learn).
Hands-on experience with data ingestion, RAG pipeline optimization, model evaluation, deployment (MLOps), and monitoring.
Deep understanding of generative AI (LLMs, embeddings, RAG, prompt engineering, and agentic reasoning) and its practical constraints (latency, cost, safety, hallucinations).
Experience turning complex business needs into "machine-ready" technical specifications and acceptance criteria.
Strong experience with major cloud platforms; hands-on knowledge of AWS (e.g., Amazon Bedrock, SageMaker) or GCP (e.g., Vertex AI) is highly desirable.
Evidence of using modern GenAI tools (Claude Code, GitHub Copilot, etc.) to significantly accelerate your development and testing process.
Job Responsibilities
Advanced Recommendation Modeling: Design and deploy recommendation algorithms that account for time-decay, breaking news signals, and short-term user intent, distinguishing them from long-term preference models used in streaming.
Generative AI Integration: Lead the development and integration of Gen AI and LLM-based models to enhance content summarization, semantic search, and personalized news briefings.
Signal Processing: Engineer complex feature sets that weigh recency, topic sensitivity, and contextual relevance (e.g., differentiating between a user following a specific stock ticker vs. a general sector interest).
Model Lifecycle Management: Own the end-to-end lifecycle of ML models, from hypothesis and training to A/B testing, deployment, and monitoring in a high
Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
8+ years of professional experience in machine learning, data science, or AI engineering.
Proven experience as a technical lead or solution architect for ML/AI projects, with accountability for end-to-end delivery in a production environment.
Strong proficiency in Python and the modern ML/AI ecosystem (e.g., PyTorch, Hugging Face, LangChain/LangGraph, Scikit-learn).
Hands-on experience with data ingestion, RAG pipeline optimization, model evaluation, deployment (MLOps), and monitoring.
Deep understanding of generative AI (LLMs, embeddings, RAG, prompt engineering, and agentic reasoning) and its practical constraints (latency, cost, safety, hallucinations).
Experience turning complex business needs into "machine-ready" technical specifications and acceptance criteria.
Strong experience with major cloud platforms; hands-on knowledge of AWS (e.g., Amazon Bedrock, SageMaker) or GCP (e.g., Vertex AI) is highly desirable.
Evidence of using modern GenAI tools (Claude Code, GitHub Copilot, etc.) to significantly accelerate your development and testing process.
Job Responsibilities
Advanced Recommendation Modeling: Design and deploy recommendation algorithms that account for time-decay, breaking news signals, and short-term user intent, distinguishing them from long-term preference models used in streaming.
Generative AI Integration: Lead the development and integration of Gen AI and LLM-based models to enhance content summarization, semantic search, and personalized news briefings.
Signal Processing: Engineer complex feature sets that weigh recency, topic sensitivity, and contextual relevance (e.g., differentiating between a user following a specific stock ticker vs. a general sector interest).
Model Lifecycle Management: Own the end-to-end lifecycle of ML models, from hypothesis and training to A/B testing, deployment, and monitoring in a high