Staff AI/Machine Learning Engineer

Tonic AI
United States
Workplace: RemoteFull timeFunction: Data Science & Machine LearningExperience: 8+ yearsSkills: ["Problem framing","Driving measurable impact","Rigor","Reproducibility","Shipping"]

Build production machine learning systems for synthetic environments used to train and evaluate AI agents. You’ll develop synthesis and de-identification models that preserve fidelity and downstream utility, train and improve NER/entity detection, and create evaluation infrastructure that grades agent outcomes. Work across longitudinal simulation, open-weight model fine-tuning, and scalable inference on sensitive customer data, partnering with frontier labs and enterprise ML teams while raising rigor for a small senior team.

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FursaFursa
Tonic AI
Tonic AI
1 month ago

Staff AI/Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 20 hours agoStatus: Live

Job Summary

Build production machine learning systems for synthetic environments used to train and evaluate AI agents. You’ll develop synthesis and de-identification models that preserve fidelity and downstream utility, train and improve NER/entity detection, and create evaluation infrastructure that grades agent outcomes. Work across longitudinal simulation, open-weight model fine-tuning, and scalable inference on sensitive customer data, partnering with frontier labs and enterprise ML teams while raising rigor for a small senior team.
Location: United States
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design and build 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 large scale while preserving format, statistical distribution, and semantic consistency.
  • •Train and improve NER/entity detection models to drive accuracy and recall across free text and structured enterprise data at scale.
  • •Create evaluation infrastructure that grades agent outcomes and discriminates between frontier models on real tasks; fine-tune and evaluate open-weight models and translate benchmarks into product/research direction.
  • •Optimize inference for efficient execution on large volumes of sensitive data inside customer environments and set technical direction for a small senior team.

Pay and Benefits

Perks:EquityPaid Leave401kHealth InsuranceDentalVisionParental LeaveRemote Work

Key Requirements

  • •8+ years building production ML systems (or PhD with 3+ years) with depth across LLMs, agents, RL, NER, or information extraction.
  • •Hands-on experience training and shipping ML models to production with practical quality measurement and readiness criteria.
  • •Experience with generative/synthesis models where output fidelity and downstream utility both matter.
  • •Strong software engineering fundamentals and fluency with modern training/evaluation stacks (including PyTorch and distributed training).
  • •Comfort working with messy, sensitive real-world data and privacy constraints; bonus for synthetic data, de-identification, or benchmark construction.
Experience:8+ yearsProduction MLLLMsAgentsReinforcement learningSynthetic data
Skills:Problem framingDriving measurable impactRigorReproducibilityShipping
Tech Stack:PyTorchDistributed trainingLLMsAgentsReinforcement learningNERInformation extraction

Company Brief

Tonic AI
Provides a platform for generating realistic synthetic data to enable safe, compliant testing and analytics. Helps engineering and data teams create privacy-preserving copies of production data for development and QA workflows.
Industry: Data Infrastructure
Company Size: Medium (51 to 250 employees)
Growth: Growth Stage Startup
Funding: Series B
Headquarters: New York, United States
Founded: 2017
WebsiteLinkedIn