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 do

Build and Ship LLM-Powered Features

• Architect and deliver production agentic workflows — both ambient (background agents that enrich, monitor, summarise, and surface insights across NewsWhip's data continuously) and interactive (user-facing tool-using agents that respond to journalist and PR analyst queries in real time).

• Own the design of LLM-powered content enrichments that feed directly into customer reporting, alerts, and intelligence briefings — turning raw news signals into structured, decision-ready outputs.

• Lead the technical direction for tool-augmented LLM systems, including MCP-compatible services, function-calling patterns, and multi-step reasoning workflows.

• Make architecture-level decisions about when to use retrieval, when to use agents, when to fine-tune, and when a smaller model or classical NLP approach is the right answer.

• Define structured prompting standards, output schemas, and reusable patterns that the wider engineering team can build on.

Drive Quality, Evaluation, and Observability

• Set the bar for how NewsWhip evaluates LLM systems — offline benchmarks, online experimentation, regression detection, and human-in-the-loop review.

• Own guardrails for safety, hallucination reduction, factual grounding, and output consistency in a domain (news and media intelligence) where accuracy is non-negotiable.

• Build observability into every LLM feature: tracing, cost tracking, latency budgets, quality metrics, and drift monitoring.

• Make pragmatic trade-offs between model quality, latency, and cost — and be accountable for them.

Retrieval, Embeddings, and Semantic Infrastructure

• Evolve the embedding and semantic search infrastructure that underpins NewsWhip's intelligence layer, including chunking strategies, hybrid search, and re-ranking.

• Improve retrieval relevance as one component of a broader agentic architecture — not as an end in itself.

Lead Technically and Influence Cross-Functionally

• Partner with Product, and UX  to translate ambiguous AI ideas into shipped features customers actually rely on.

• Lead technical design discussions and represent the AI team in architecture decisions.

• Raise the bar through code review, mentorship, and writing — help less experienced engineers grow into strong AI practitioners.

• Stay close to the frontier: evaluate emerging models, frameworks, and techniques and bring the right ones into the stack.

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).