Staff Applied Scientist – AI Evaluation, Trust

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🕒 April 23

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Sayari

WebsiteLinkedInAll Job Openings

1 - 10 employees

🔬 Science

🤝 B2B

Science • B2B

Sayari is a French environmental consultancy and research bureau that helps organizations operationalize the latest scientific advances for sustainable decision-making. The firm develops environmental data, conducts life-cycle assessments, and builds strategies for product environmental impact, biodiversity, and water resilience. Sayari serves public actors, large companies, SMEs and startups by delivering bespoke studies, tools and trainings to anticipate regulation, improve performance, and enhance attractiveness to investors and stakeholders. Headquartered in Saint-Germain-en-Laye, France, Sayari participates in national initiatives such as AGRIBALYSE and environmental footprint committees.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ 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 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