Senior Applied AI/ML Scientist, Sprout Social

• Even if your background doesn’t match the exact requirements, we encourage you to apply and share any relevant skills in a cover letter
• 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
• 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

Desirable

• NewsWhip by Sprout Social is looking for a Senior Applied AI/ML Scientist to join its AI, Data and Intelligence Business Unit
• 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
• 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
• Within 1 month, you’ll plant your roots, including:
• 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
• Within 3 months, you’ll start hitting your stride by:
• Take ownership of a core LLM-powered feature or subsystem
• Implement measurable improvements to prompt quality, retrieval relevance, or model performance
• Contribute to improving our AI evaluation and testing strategy
• Lead at least one technical design discussion related to AI architecture
• Within 6 months, you’ll be making a clear impact through:
• Drive the end-to-end delivery of a significant AI feature from concept to production launch
• Establish reusable patterns or tooling that improve LLM development velocity and reliability
• Demonstrate measurable impact on quality metrics (e.g., reduced hallucination rates, improved relevance, latency, or cost efficiency)
• Within 12 months, you’ll make this role your own by:
• Be recognized as a go-to expert for LLM systems and applied AI best practices within the team
• Help shape the longer-term AI architecture and technical roadmap
• Mentor other engineers on prompt design, evaluation, and AI system reliability
• Surprise us - propose and deliver AI innovations that meaningfully change how customers use NewsWhip
• Of course what is outlined above is the ideal timeline, but things may shift based on business needs and other projects and tasks could be added at the discretion of your manager