Tonic AI - Staff AI/Machine Learning Engineer
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. • Bonus: synthetic data generation, data privacy or de-identification, or benchmark construction.
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.
Benefits
• Unlimited paid time off • 401k plan with employer contribution • Medical, dental, and vision insurance • Generous parental leave policy • Remote-friendly work environment
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