Sr. Applied AI/ML Scientist

Description

NewsWhip by Sprout Social is looking for a Senior Applied AI/ML Scientist to join its AI, Data and Intelligence Business Unit.

Why join NewsWhip by Sprout Social’s Data Science and AI team?

Most LLM applications are wrappers around a chat box. Ours aren't.

NewsWhip processes the world's news in real time - millions of articles, posts, and signals every day - and turns that firehose into predictive intelligence that journalists, PR leaders, and global brands rely on to make decisions before the news breaks. The interesting problems live everywhere in that pipeline: ambient agents that monitor and enrich content as it flows in, retrieval systems that have to be fast and correct on a corpus that changes by the minute, evaluation frameworks for a domain where hallucination has real-world consequences, and reporting workflows where LLM-generated insights need to stand up to scrutiny by professional analysts.

You'll work at the intersection of product, data, and engineering with the autonomy to make architectural decisions and the support of a company that takes AI quality seriously. If you want to build LLM systems that do more than answer questions - systems that reason, retrieve, enrich, and act across one of the most demanding content domains there is — this is the role.

What you’ll bring

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.

Minimum Qualifications

• 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).