We’re looking for an experienced and highly technical Senior Applied AI/ML Scientist who embraces challenges, practices a growth mindset, and is eager to collaborate with a variety of stakeholders to provide data-driven solutions to business leaders and to NewsWhip’s by Sprout Social customers
Bachelor’s in Data Science, Computer Science, Machine Learning, AI, or a related discipline
Experience with AI orchestration frameworks (LangChain, LlamaIndex, LangGraph, etc.)
Familiarity with Model Context Protocol (MCP) or tool-calling architectures
Experience building agentic workflows or tool-using systems
Knowledge of semantic search and content retrieval systems
Experience with cloud platforms
Background in media analytics, content intelligence, or large-scale text processing
Experience in startup or high-growth environments
How you’ll grow
6+ years of experience building and operating production software systems
2+ years hands-on experience shipping LLM-powered features in real-world applications
Strong backend engineering skills (Python preferred)
Experience with several of the following
Large Language Model APIs (OpenAI, Anthropic, open-weight models, etc.) and transformer-based techniques
Embedding models and similarity search
Vector databases (ChromaDB, Pinecone, Weaviate, etc.)
Prompt engineering and structured output techniques
LLM evaluation frameworks and automated testing
LLMOps practices (monitoring, versioning, observability using Langfuse, Datadog, etc.)
Track record of owning a system end-to-end in production, not just contributing to one
Experience making and defending architectural trade-offs (model choice, build vs. buy, latency vs. quality)
Experience mentoring engineers or leading technical design reviews
Experience working closely with Product and UX on feature delivery
Complete onboarding and gain a deep understanding of NewsWhip’s product, data model, and AI roadmap
Meet and learn from assigned onboarding resources
Set clear expectations and goals with your manager
Familiarize yourself with our existing LLM infrastructure, evaluation practices, and vector systems
Ship your first meaningful improvement or feature iteration to production
Become familiar with our existing features, available data, and best practices