POSITION DESCRIPTION

Sayari builds AI systems for high-consequence analytical work where being "wrong" carries real-world weight. We are looking for a Staff or Principal Applied Scientist to join our AI Innovation Group as the trusted expert on AI Evaluation and Trust. You will own the "Judgment Layer" of our system: building the specialized judge models, statistical benchmarks, and multi-turn frameworks that ensure our agents act with the high bar of trustworthiness required by our national security and enterprise customers.

JOB RESPONSIBILITIES

• Lead the development of specialized "judge models," moving from general-purpose frontier models to architectures purpose-built for evaluation and failure mode detection.
• Design and execute rigorous scoring pipelines and empirical threshold calibrations for agentic systems, including multi-turn conversation and Graph RAG reasoning.
• Establish domain-specific evaluation frameworks that measure whether a system can perform the work of human experts rather than just passing general capability benchmarks.
• Own the full lifecycle of evaluation data, from designing annotation infrastructure and protocols to deploying evaluation services into production.
• Research and implement advanced techniques in Mixture-of-Experts (MoE) routing, expert specialization evaluation, and ensemble calibration.
• Collaborate cross-functionally with Product, Data Engineering, and the SVP of AI to translate complex statistical uncertainty into clear, actionable product signals.
• Act as a technical leader and "Scientific Conscience" within the AI pod, ensuring every AI-driven risk signal is backed by an empirical derivation story.

SKILLS & EXPERIENCE

Required:

• 10+ years of Machine Learning experience with a focus on Deep Neural Network activities, evaluating model performance & trust.  
• 1-2+ years’ experience focused on post-training activities
• 1+ year experience creating benchmarks to evaluate LLMs
• Technical Mastery: Deep expertise in LLM-as-judge architectures, multi-turn evaluation, and Reinforcement Learning (RL/RLHF/RLAIF).
• Statistical Rigor: Mastery of statistics and experimental design, including significance testing, distribution analysis, and inter-rater reliability.
• Architectural Depth: Experience with Mixture-of-Experts (MoE) systems, routing behavior, and expert specialization.
• Builder Mindset: Proven ability to own the path from data collection to production deployment; we are a small team and every role is "hands-on."
• Domain Fluency: Understanding of Graph RAG and the unique challenges of evaluating non-deterministic, agentic workflows.

Preferred:

• Judgment Task Models: Experience building, fine-tuning (LoRA, etc.), or pre-training models specifically for judgment, preference modeling, or classification tasks.
• Domain Context: Background in cognitive science, intelligence community tradecraft, or research literature on expert judgment under uncertainty.
• Infrastructure at Scale: Experience building or managing large-scale annotation infrastructure and quality assurance protocols.
• Academic/Research Track Record: A record of published research or recognized work in preference modeling or AI alignment.

The target base salary for this position is $195,000-$225,000 plus company bonus and equity. Final offer amounts are determined by multiple factors including location, local market variances, candidate experience and expertise, internal peer equity, and may vary from the amounts listed above.

Benefits: 

Senior Data Engineer: Build and maintain ETL pipelines to process and export record data to
Sayari Graph application; Develop and improve entity resolution processes; Implement logic to
calculate and export risk information; Work with product team and other development teams to
collect and refine requirements; Run and maintain regular data releases. Requirements: Bachelor's
degree, or foreign equivalent, in Data Engineering, Computer Science or closely related field. 3
years of experience as Software Data Engineer or any occupation in which the required software
data engineering experience is gained. Must have the following skills/knowledge: Python; a JVM
programming language such as Scala; a SQL database (E.G., Postgres); a NoSQL Database (e.g.,
Hbase, Cassandra, Elasticsearch, or memgraph, etc.); designing, maintaining, and orchestrating
ETL pipelines (e.g., Apache spark or Apache airflow) in cloud based environments (e.g., GCP,
AWS, or Azure). FT. Fully remote position. Telecommuting from any location in the U.S. is
permitted. Wage: $176,985 per year. Apply online or mail resumes to company: Sayari Labs, Inc,
1152 15th St. NW, Suite 910, Washington, DC 20005. Attn: Emily S.

The base salary for this position is $176,985-$176,985 plus annual bonus and equity.

Benefits: 

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.

The target base salary for this position is $185,000-$200,000 plus company bonus and equity. Final offer amounts are determined by multiple factors including location, local market variances, candidate experience and expertise, internal peer equity, and may vary from the amounts listed above.

Benefits: