Machine Learning Lead (AI Data Labeling)

Join NewtonX, a rapidly expanding company in the AI data annotation and reinforcement learning space. As a Machine Learning Lead, you will be the technical owner of data quality, partnering with the Program Lead to convert customer requirements into concrete technical specs and defining quality metrics. You will also serve as the technical counterpart to the customer's ML, applied science, and product teams. This is a foundational role with opportunities for growth as the business expands.

• Assurer la qualité des données produites pour l'entraînement et l'évaluation des modèles d'IA, en concevant des tâches et des rubriques qui produisent un signal d'entraînement utile.
• Collaborer avec le responsable du programme pour convertir les exigences du client en spécifications techniques concrètes, y compris les profils d'experts, les arbres de sélection, les interfaces de tâches, les modèles de tâches, les rubriques de contrôle qualité.
• Servir de contrepartie technique aux équipes ML, de science appliquée et de produit du client, en défendant les choix de conception technique en temps réel.

• If the profile above describes you and your passions, we'd love to hear from you!
• Strong programming foundation: read and reason about an eval harness, write Python comfortably, work with model APIs, prototype scoring pipelines. Not a production engineer, but not hands-off
• At least 2 years working hands-on with RL, including how tool-use trajectories are rewarded and evaluated. If you're not fluent in RL, this isn't the role — it's the foundation of the core judgment you'll be making
• Strong written communication: methodology sections, technical reports, and specs that hold up to expert review
• Working fluency across modern LLM post-training and evaluation: SFT, RLHF/preference data quality, RLVR, chain-of-thought, eval harness construction, contamination handling, statistical significance, and agentic/tool-use evaluation
• Statistical fluency: you know when an effect is real vs. noise and can defend a sample size or significance threshold
• Client-facing presence: you've defended technical design choices in real time to skeptical audiences and adjusted scope without losing rigor. Range matters — you can talk to a Series B CTO and a Fortune 100 AI lead in the same week
• Deep applied ML experience centered on post-training human data — you've owned a human-data workstream as an applied scientist or ML engineer
• Genuine understanding of how training data becomes model behavior — you can reason about what a model will learn from a given dataset, not just whether the data meets spec