Staff AI/Machine Learning Engineer

Tonic.ai builds the data infrastructure behind modern AI, focusing on de-identifying real enterprise data for safe use in training. As a Staff AI Engineer, you will own the models ensuring data trustworthiness and work on developing systems for generating synthetic environments and training models that handle sensitive data.

Responsibilities:

• Design and build the systems that generate longitudinally coherent synthetic environments for agent training and evaluation, including persona modeling, task generators, and verifiable ground truth
• Build and maintain synthesis models that generate realistic replacement values at very large scale, preserving format, statistical distribution, and semantic consistency so de-identified data stays useful downstream
• Train and improve the NER models behind our entity detection, driving accuracy and recall across free text, structured fields, and mixed enterprise data at scale
• Build evaluation infrastructure that grades agent outcomes, not just traces, and produces real discrimination between frontier models on real tasks
• Fine-tune and evaluate open-weight models on Tonic-generated data, and turn benchmark results into product and research direction
• Expand coverage into new domains, languages, and entity types, and handle the long tail of formats and edge cases that real customer data throws off
• Own model evaluation across the board: precision and recall on detection, utility preservation on synthesis, and outcome-level grading for agents
• Optimize inference so models run efficiently on large volumes of sensitive data inside customer environments
• Partner directly with frontier labs and enterprise ML team to turn hard data problems into shipped model improvements
• Set technical direction for a small, senior team and raise the bar on rigor, reproducibility, and shipping

Requirements:

• 8+ years (or PhD with 3+ years) building production ML systems, with real depth in some combination of LLMs, agents, RL, NER, or information extraction
• Hands-on experience training and shipping models to production, and a pragmatic bar for quality: you know how to measure it, where it breaks, and when it's good enough to ship
• Experience with generative or synthesis models where output fidelity and downstream utility both matter, not just plausibility
• Strong software engineering fundamentals. You write code others build on
• Fluency with modern training and eval stacks (PyTorch, distributed training, standard agent and benchmark frameworks)
• Comfort working with messy, sensitive, real-world data and the privacy constraints that come with it
• A track record of framing ambiguous problems and driving them to measurable, shipped results
• synthetic data generation
• data privacy or de-identification
• benchmark construction