Senior AI Scientist (Remote) — Synthetic Data & Evaluation Systems

Tonic.ai; San Francisco (CA); Sur place; USD 180 000 - 260 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Equity

Unlimited PTO

401k with employer contribution

Medical, dental, and vision insurance

Remote-friendly work environment

Résumé du poste

Tonic.ai in San Francisco builds data infrastructure for AI, creating synthetic environments and de-identifying enterprise data to enable safe model training and evaluation.

As a senior ML engineer, you will design systems for synthetic data generation, improve NER and evaluation, and collaborate with frontier labs and enterprise ML teams to ship model improvements. This role requires deep production ML experience, strong software fundamentals, and a track record of shipping impactful models.

Qualifications

• 5+ years (or PhD with 1+ years) building production ML systems.
• Experience with LLMs, agents, RL, or information extraction.
• Hands-on experience training and shipping models to production.
• Experience with generative or synthesis models where fidelity matters.
• Strong software engineering fundamentals.
• Fluency with PyTorch and distributed training.

Responsabilités

• Design and build systems that generate longitudinally coherent synthetic environments for agent training and evaluation.
• Build and maintain synthesis models that generate realistic replacement values at very large scale.
• Train and improve the NER models behind entity detection across diverse data.
• Build evaluation infrastructure that grades agent outcomes and discriminates frontier models.
• Fine-tune open-weight models on Tonic-generated data and translate results into product directions.
• Expand coverage into new domains, languages, and entity types.
• Own model evaluation across precision/recall and downstream utility.
• Optimize inference for large volumes of sensitive data in customer environments.

Description du poste

Tonic.ai in San Francisco builds data infrastructure for AI, creating synthetic environments and de-identifying enterprise data to enable safe model training and evaluation.

As a senior ML engineer, you will design systems for synthetic data generation, improve NER and evaluation, and collaborate with frontier labs and enterprise ML teams to ship model improvements. This role requires deep production ML experience, strong software fundamentals, and a track record of shipping impactful models.