обновлено 2 часа назад
Staff AI Engineer
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff AI Engineer (LLMs/Agents/NER): Building synthesis models, entity-detection systems, and synthetic environments for agent training and evaluation with an accent on production ML, privacy-sensitive data, and model quality. Focus on designing outcome-level evaluation infrastructure, fine-tuning open-weight models, optimizing inference at scale, and setting technical direction for a senior team.
Location: Remote
Company
builds data infrastructure for AI, including de-identification, synthetic data generation, and tools for safe use of enterprise data in software development, model training, and evaluation.
What you will do
- Design systems that generate longitudinally coherent synthetic environments for agent training and evaluation, including persona models, task generators, and verifiable ground truth.
- Build and maintain synthesis models that preserve format, statistical distribution, and semantic consistency in replacement data.
- Train and improve NER models for entity detection across free text, structured fields, and mixed enterprise data.
- Build evaluation infrastructure that grades agent outcomes and differentiates frontier models on real tasks.
- Fine-tune and evaluate open-weight models on generated data, translating benchmark results into product and research direction.
- Optimize inference for large volumes of sensitive data and partner with frontier labs and enterprise ML teams on shipped model improvements.
Requirements
- 8+ years of experience building production ML systems, or a PhD with 3+ years of relevant experience.
- Deep experience in areas such as LLMs, agents, reinforcement learning, NER, or information extraction.
- Hands-on experience training and deploying production models, including model measurement, evaluation, and quality improvement.
- Experience with generative or synthesis models where output fidelity and downstream utility are critical.
- Strong software engineering fundamentals and experience with PyTorch, distributed training, and agent or benchmark frameworks.
- Ability to work with messy, sensitive real-world data and drive ambiguous problems to measurable results.
Nice to have
- Experience with synthetic data generation, data privacy, de-identification, or benchmark construction.
Culture & Benefits
- Remote work environment.
- Work directly with frontier AI labs and enterprise ML teams.
- Build products used with sensitive data in healthcare, financial services, logistics, education, and e-commerce.
- Collaborate with a small, senior team and contribute to technical direction, rigor, reproducibility, and shipping standards.
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