Senior Applied Scientist (Remote, US)

About Sayari:

Sayari is the judgment infrastructure for trustworthy AI in economic security and commercial risk. The Sayari Commercial World Model resolves 11.7B+ primary-source records from 250+ jurisdictions forming the ground truth of global commerce. A Judgment Ontology, encoding over a decade of investigative tradecraft, and Superconductor, an agentic orchestration platform, deliver AI that reasons like an expert analyst, shows its work, and traces every finding to its source. Trusted by U.S. Customs and Border Protection, HM Revenue & Customs, and Fortune 500 enterprises, Sayari is used by thousands of professionals across 35+ countries to secure supply chains and dismantle illicit networks. Headquartered in Washington, D.C., with offices in London, Singapore, Tokyo, and Tel Aviv.

Position Description

Sayari's AI Innovation Lab is building the proprietary models that power our understanding of global corporate ownership, trade networks, and entity resolution. We're an early-stage team within a growth-stage company, which means you'll have real ownership over hard problems and the room to shape how we solve them.

We're hiring a Senior Applied Scientist to own domain-specific model development end to end—from data strategy through training, evaluation, and deployment. You'll work closely with our Technical Lead on architecture and strategy decisions, and alongside a small team of AI/ML engineers who ship together.

This isn't a research role. You'll be building models that go to production on messy, high-stakes data—corporate records, trade manifests, beneficial ownership structures. If you've only worked with clean benchmark datasets, this isn't the right fit.

Job Responsibilities

• Model Ownership: Design and execute fine-tuning strategies (LoRA, full fine-tuning) and own the experimentation cycle from hypothesis through production deployment.
• Data Strategy: Build and refine data collection and labeling strategies for complex, messy domains, including designing auto-labeling pipelines.
• Architecture: Evaluate and select base models (proprietary and open-source) for domain-specific tasks, focusing on small language models (SLMs).
• Deployment: Deploy fine-tuned models to production using cloud ML platforms (Vertex AI, SageMaker, or equivalent).
• Evaluation: Develop rigorous evaluation frameworks that measure real-world performance on agentic workflows, not just benchmarks.

Skills & Experience

• 5+ years of experience in applied ML with a focus on model training and fine-tuning in a commercial setting.
• 2+ years of hands-on experience with LoRA and full fine-tuning—you have shipped these, not just read about them.
• Track record of deploying fine-tuned models to production on cloud ML platforms.
• High comfort level working with messy, unstructured data and designing labeling strategies from scratch.
• Proficiency with HuggingFace, PyTorch, and at least one cloud ML platform.
• Strong opinions about base model selection, held loosely—you can articulate tradeoffs between architectures.

Benefits

• 100% fully paid medical, vision, and dental for employees and their dependents
• Generous time off; we observe all US federal holidays, close our office for a win