Senior Applied Scientist (Remote, US)

Sayari

6h • $185,000 - $200,000 • Remote

About The Position

Sayari is a venture-backed and founder-led global corporate data provider and commercial intelligence platform that serves financial institutions, legal and advisory service providers, multinationals, journalists, and governments. Thousands of analysts and investigators in over 30 countries rely on our products to safely conduct cross-border trade, research front-page news stories, confidently enter new markets, and prevent financial crimes such as corruption and money laundering.
Our company culture is defined by a dedication to our mission of using open data to prevent illicit commercial and financial activity, a passion for finding novel approaches to complex problems, and an understanding that diverse perspectives create optimal outcomes. We embrace cross-team collaboration, encourage training and learning opportunities, and reward initiative and innovation. If you like working with supportive, high-performing, and curious teams, Sayari is the place for you.
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.

Requirements

• 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.

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.

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 winter break (12/24-12/31), in addition to granting 18 PTO days and 10 sick days
• Outstanding compensation package; competitive commissions for revenue roles and quarterly bonuses for non-revenue positions
• A strong commitment to diversity, equity, and inclusion
• Eligibility to participate in additional benefits such as 401k match up to 5%, 100% paid life insurance (up to $100,000 coverage),, and parental leave
• A collaborative and positive culture - your team will be as smart and driven as you
• Limitless growth and learning opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed