You will build and maintain ML models, apply NLP techniques, design experiments, and partner with engineering for model deployment.
Draup is a Series A-funded agentic AI company building the intelligence layer for how global enterprises make workforce and go-to-market decisions. We work with 250+ enterprise clients — including 5 of the Fortune 10 — processing 1B+ job descriptions, 850M+ professional profiles, and signals from 100+ labor databases.
We are now building our Silicon Valley engineering team — a small, senior group focused on next-generation AI research and product.
Location: San Francisco, SoMa — minimum 4 days per week in-office.
What you'll do
• Build and maintain ML models for classification, extraction, trend detection, and predictive scoring on large structured and unstructured datasets.
• Design experiments and benchmarks to measure model accuracy, reduce bias, and validate outputs at scale.
• Apply NLP techniques — embeddings, NER, text classification — to real-world data pipelines.
• Partner with engineering to move models from experimentation to production; own monitoring and drift detection.
• Build evaluation frameworks for AI-generated outputs across multiple product use cases.
What we require
• BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field.
• 3–5 years of applied data science; minimum 2 years working with NLP or large-scale text data in production.
• Strong Python (pandas, scikit-learn, PyTorch or TensorFlow); proficient in SQL.
• Demonstrated track record of shipping models into production, not just producing analysis.
• Experience with embedding models and semantic similarity at enterprise scale.
• No visa sponsorship. Must be authorized to work in the US without current or future employer sponsorship.
Similar Jobs
Security • Software
Designs, builds, evaluates, and deploys machine learning, LLM, and agentic AI capabilities for cybersecurity detection, investigation, and response. Analyzes complex security data, develops models and evaluation frameworks, creates RAG and retrieval workflows, and partners with engineering teams to productionize and monitor AI systems. The role requires addressing adversarial, noisy data and balancing accuracy, explainability, latency, reliability, and operational impact.
Top Skills:
DatabricksEmbeddingsLarge Language Models (Llms)Machine LearningPysparkPythonRetrieval-Augmented Generation (Rag)SQLVector Databases
Artificial Intelligence • HR Tech • Information Technology • Software • Database • Cybersecurity • Big Data Analytics
Own end-to-end applied research on business ontologies, knowledge graphs, semantic layers, and AI agents over enterprise data. Develop methods using schema matching, entity resolution, LLMs, graph neural networks, and relational models; build benchmarks, evaluation systems, and prototypes adopted by product teams. Collaborate with universities, publish research or open-source work, communicate findings clearly, and mentor interns. The hybrid Palo Alto role requires up to 10% travel.
Top Skills:
Api DiscoveryCypherEntity ResolutionGpusGraph Neural NetworksGraphragKnowledge GraphsLarge Language ModelsMcpOntologiesOwlPythonPyTorchRdfSchema MatchingShaclSparkSparqlSQLText-To-SqlVector Search
Artificial Intelligence • HR Tech • Information Technology • Software • Database • Cybersecurity • Big Data Analytics
Lead applied AI research focused on learning business ontologies, semantic layers, and knowledge graphs from enterprise data. Develop graph and relational foundation models, LLM systems over structured data, grounded agents, evaluation benchmarks, and governed business actions. Define research directions, collaborate with universities and SAP product teams, publish results, ship prototypes into products, mentor scientists, and continue hands-on experimentation and coding.
Top Skills:
Ai AgentsCausal InferenceEntity ResolutionGraph Neural NetworksGraph-Grounded RetrievalJaxJouleKnowledge GraphsLarge Language ModelsOdataOntologiesOwlProcess MiningProperty GraphsPythonPyTorchRdfReinforcement LearningRelational Deep LearningRelational Foundation ModelsSap Business Data CloudSap Knowledge GraphSchema LinkingSchema MatchingSemantic Data IntegrationShaclSparqlSQLTabular Foundation ModelsText-To-Sql
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
