3 дня назад
Machine Learning Engineer (LLM)
180 000 - 225 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Machine Learning Engineer (LLM): Fine-tuning transformer-based and multimodal models through automation pipelines and deploying real-time machine learning systems in production with an accent on large-scale data processing, feature engineering, and model evaluation. Focus on building scalable fine-tuning frameworks, optimizing model size, cost, and latency, and monitoring model performance in real time.
Location: United States — Remote
Salary: $180,000–$225,000 USD annually
Company
is an early-stage AI company founded in 2022 that develops trustworthy and useful intelligent technologies powered by automation, machine learning, and knowledge graphs.
What you will do
- Fine-tune transformer-based, large language, and multimodal models using production automation pipelines.
- Process large datasets and engineer features to improve model performance.
- Build frameworks for iterating on and evaluating model versions based on ranking, accuracy, and latency.
- Deploy machine learning models at scale in collaboration with software engineers.
- Implement real-time monitoring, maintenance, and updates for production models.
- Collaborate with data scientists, software developers, designers, and business analysts to deliver machine learning solutions.
Requirements
- 3+ years of experience developing and deploying machine learning models in production.
- Experience designing, developing, and operating fine-tuning pipelines in production environments.
- Experience with large-scale data processing and distributed systems.
- Strong Python programming skills.
- Proficiency with machine learning libraries such as PyTorch and TensorFlow.
- Must be based in the United States.
Nice to have
- Experience optimizing models for size, cost, and latency.
- Startup experience.
Culture & Benefits
- Early-stage equity.
- Comprehensive health insurance.
- Generous paid time off.
- Remote and in-person collaboration across Austin, New York City, and the Bay Area.
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