{"id":2000025,"url":"https://alion.io/job/sprout-social-sr-applied-aiml-scientist-b2b","title":"Sr. Applied AI/ML Scientist (B2B)","company":{"id":3769,"name":"Sprout Social","domain":"sproutsocial.com","url":"https://alion.io/company/sprout-social","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":88,"open_postings":4,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":26600,"max":33300,"currency":"PLN","period":"month","gross":false,"usd_annual":102792},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Datadog","optional":false},{"name":"Embeddings","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Langfuse","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Weaviate","optional":false},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-07T08:47:25Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-31T02:46:36Z","board_verified":true,"closed_at":null,"days_open":23,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":23},"description":"Description\nNewsWhip by Sprout Social is looking for a Senior Applied AI/ML Scientist (LLM Applications) to join its AI, Data and Intelligence Business Unit. \nWhy join NewsWhip by Sprout Social’s Data Science and AI team? \nMost LLM applications are wrappers around a chat box. Ours aren't. \nNewsWhip 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.\nYou'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.\nWhat you'll do\nBuild and Ship LLM-Powered Features\nArchitect 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).\n\nOwn 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.\n\nLead the technical direction for tool-augmented LLM systems, including MCP-compatible services, function-calling patterns, and multi-step reasoning workflows.\n\nMake 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.\n\nDefine structured prompting standards, output schemas, and reusable patterns that the wider engineering team can build on.\n\nDrive Quality, Evaluation, and Observability\nSet the bar for how NewsWhip evaluates LLM systems - offline benchmarks, online experimentation, regression detection, and human-in-the-loop review.\n\nOwn guardrails for safety, hallucination reduction, factual grounding, and output consistency in a domain (news and media intelligence) where accuracy is non-negotiable.\n\nBuild observability into every LLM feature: tracing, cost tracking, latency budgets, quality metrics, and drift monitoring.\n\nMake pragmatic trade-offs between model quality, latency, and cost - and be accountable for them.\n\nRetrieval, Embeddings, and Semantic Infrastructure\nEvolve the embedding and semantic search infrastructure that underpins NewsWhip's intelligence layer, including chunking strategies, hybrid search, and re-ranking.\n\nImprove retrieval relevance as one component of a broader agentic architecture - not as an end in itself.\n\nLead Technically and Influence Cross-Functionally\nPartner with Product, and UX to translate ambiguous AI ideas into shipped features customers actually rely on.\n\nLead technical design discussions and represent the AI team in architecture decisions.\n\nRaise the bar through code review, mentorship, and writing - help less experienced engineers grow into strong AI practitioners.\n\nStay close to the frontier: evaluate emerging models, frameworks, and techniques and bring the right ones into the stack.\n\nWhat you’ll bring\nWe’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. \nMinimum Qualifications\n6+ years of experience building and operating production software systems.\n\n2+ years hands-on experience shipping LLM-powered features in real-world applications.\n\nStrong backend engineering skills (Python preferred). \n\nExperience with several of the following:\n\nLarge Language Model APIs (OpenAI, Anthropic, open-weight models, etc.) and transformer-based techniques\n\nEmbedding models and similarity search\n\nVector databases (ChromaDB, Pinecone, Weaviate, etc.)\n\nPrompt engineering and structured output techniques\n\nLLM evaluation frameworks and automated testing\n\nLLMOps practices (monitoring, versioning, observability using Langfuse, Datadog, etc.)\n\nTrack record of owning a system end-to-end in production, not just contributing to one.\n\nExperience making and defending architectural trade-offs (model choice, build vs. buy, latency vs. quality).\n\nExperience mentoring engineers or leading technical design reviews.\n\nExperience working closely with Product and UX on feature delivery.\n\nPreferred Qualifications\nBachelor’s in Data Science, Computer Science, Machine Learning, AI, or a related discipline.\n\nExperience with AI orchestration frameworks (LangChain, LlamaIndex, LangGraph, etc.).\n\nFamiliarity with Model Context Protocol (MCP) or tool-calling architectures.\n\nExperience building agentic workflows or tool-using systems.\n\nKnowledge of semantic search and content retrieval systems.\n\nExperience with cloud platforms.\n\nBackground in media analytics, content intelligence, or large-scale text processing.\n\nExperience in startup or high-growth environments.\n\nHow you’ll grow\nWithin 1 month, you’ll plant your roots, including:\nComplete onboarding and gain a deep understanding of NewsWhip’s product, data model, and AI roadmap.\n\nMeet and learn from assigned onboarding resources.\n\nSet clear expectations and goals with your manager.\n\nFamiliarize yourself with our existing LLM infrastructure, evaluation practices, and vector systems.\n\nShip your first meaningful improvement or feature iteration to production.\n\nBecome familiar with our existing features, available data, and best practices.\n\nWithin 3 months, you’ll start hitting your stride by:\nTake ownership of a core LLM-powered feature or subsystem.\n\nImplement measurable improvements to prompt quality, retrieval relevance, or model performance.\n\nContribute to improving our AI evaluation and testing strategy.\n\nLead at least one technical design discussion related to AI architecture.\n\nWithin 6 months, you’ll be making a clear impact through:\nDrive the end-to-end delivery of a significant AI feature from concept to production launch.\n\nEstablish reusable patterns or tooling that improve LLM development velocity and reliability.\n\nDemonstrate measurable impact on quality metrics (e.g., reduced hallucination rates, improved relevance, latency, or cost efficiency).\n\nWithin 12 months, you’ll make this role your own by:\nBe recognized as a go-to expert for LLM systems and applied AI best practices within the team.\n\nHelp shape the longer-term AI architecture and technical roadmap.\n\nMentor other engineers on prompt design, evaluation, and AI system reliability.\n\nSurprise us - propose and deliver AI innovations that meaningfully change how customers use NewsWhip.\n\nOf 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.\nOur Benefits Program\nWe’re proud to regularly be recognized for our team, product and culture. Our benefits program includes:\n Stock Ownership (RSUs): Eligibility for Restricted Stock Units (RSUs)\nGenerous Paid Time Off: 26 paid days off annually, in addition to bank holidays and extra company-wide \"Rest & Recharge\" days.\nPaid Parental Leave: We support all growing families with 20 weeks of paid leave for birthing parents and 16 weeks of paid leave for non-birthing parents.\n Lifestyle Spending Account (LSA): An annual $600 USD fund to support your personal well-being. This can be used for gym memberships, health expenses, hobbies, or other wellness needs.\nRemote & Hybrid Work Support: A flexible, remote-first model supported by a one-time $550 USD home office setup stipend and a monthly internet reimbursement.\nMental Health & Well-being: Access to dedicated mental health resources and company-wide wellness programs.\nInternational Travel: Opportunities to travel to our Chicago HQ for team meetups and collaboration.\nKraków Office Perks: A daily 50 zł lunch stipend when you choose to work from our office.\n*This list is for informational purposes only. Benefit offerings are discretionary and subject to change and do not constitute a contract or guarantee of benefits.\nOur salary ranges reflect the expected earning potential for this role. Individual pay is based on geographic zone, relevant experience, and skills.\nOur current hiring range for this role: 26,600 PLN - 33,300 PLN Net monthly. Offers are made within this range, with opportunities to grow within the broader band based on performance and impact.\nWe share both our current hiring range and our broader geographic salary bands to provide transparency into our compensation philosophy. This ensures you understand not only your starting potential but also the long-term growth opportunities available as you progress in your role.\nThe full base pay range for this role is PLN 26,600 PLN - 40,000 PLN Net monthly.\nThese ranges were determined by a market-based compensation approach; we used data from trusted third-party compensation sources to set equitable, consistent, and competitive ranges. We also evaluate compensation bi-annually, identify any changes in the market and make adjustments to our ranges and existing employee compensation as needed.\nIf you require a reasonable accommodation for any part of the interview process or to submit your application, please email us at . Include the nature of your request and your preferred contact information. We'll do everything we can to support your success during our recruitment process while upholding your privacy. Please note that only inquiries regarding accommodations will receive a response from this email address; other inquiries will not be addressed (e.g., you send your resume but are not requesting an accommodation). \nWhenever possible, we want to provide team members the flexibility to work in the location that makes the most sense for them. If you prefer an office setting, this role may be based in our Kraków location. If you prefer to work remotely from another location within Poland, we will accommodate you as best as possible. \nSprout Social Inc. and its subsidiaries process personal data submitted through your application to assess your qualifications for employment and to inform our hiring decision and, where applicable, for required governmental reporting. 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