Staff AI/Machine Learning Engineer

About the Company

Tonic builds the data infrastructure behind modern AI. We generate the synthetic environments that agents are trained and tested in, and we de-identify real enterprise data so it can be used safely in training and evaluation.

Responsibilities

• Design and build systems that generate longitudinally coherent synthetic environments for agent training and evaluation
• Build and maintain large-scale synthesis models that preserve format, statistical distribution, and semantic consistency
• Train and improve NER models for entity detection across free text and structured fields
• Build evaluation infrastructure to grade agent outcomes and produce discrimination between frontier models
• Fine-tune and evaluate open-weight models on Tonic-generated data
• Optimize inference for efficient model execution on large volumes of sensitive data
• Set technical direction for a senior team and partner with frontier labs and enterprise ML teams

Requirements

• 8+ years of experience building production ML systems or a PhD with 3+ years of experience
• Deep expertise in LLMs, agents, RL, NER, or information extraction
• Hands-on experience training and shipping models to production
• Experience with generative or synthesis models focusing on output fidelity and utility
• Strong software engineering fundamentals and proficiency with PyTorch and distributed training
• Ability to work with messy, sensitive, real-world data under privacy constraints

Preferred Qualifications

• Experience with synthetic data generation
• Background in data privacy or de-identification
• Experience in benchmark construction