4 дня назад
Staff Machine Learning Engineer (AI)
210 000 - 250 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Machine Learning Engineer (AI/LLM): Designing scalable machine learning pipelines and production infrastructure for agentic AI applications across insurance use cases with an accent on LLM workloads, reliability, and compliance. Focus on building reusable ML platform components, automating research-to-production workflows, and implementing observability, drift detection, testing, and rollback strategies.
Location: Remote
Salary: $210,000–$250,000 annually, plus stock options, benefits, and additional perks.
Company
develops an AI-native platform using agentic AI to transform the insurance policy lifecycle.
What you will do
- Design and implement efficient, scalable machine learning pipelines for multiple insurance use cases.
- Serve as a technical lead for junior engineers and provide mentorship and guidance.
- Establish production-grade deployment standards focused on scalability, reliability, and compliant insurance data handling.
- Build reusable infrastructure components and CI/CD pipelines for ML and LLM workloads.
- Enable rapid experimentation and reliable transition from research to production.
- Define observability, testing, monitoring, model and data drift detection, logging, and automated rollback practices.
Requirements
- At least 8 years of experience as a Machine Learning Engineer or in a similar role.
- At least 2 years of experience working extensively with large language models.
- Expertise designing scalable machine learning pipelines for classical ML and LLM applications.
- Experience automating and monitoring ML workflows in production.
- Hands-on experience with cloud platforms, model deployment, cloud resources, and APIs for data intake, storage, and processing.
- Strong communication skills and the ability to explain complex findings to non-technical audiences.
Culture & Benefits
- First-principles approach to solving complex problems.
- Fast iteration with a focus on proving solutions in production.
- Culture of learning, listening to users, and adapting ideas.
- Stock options, benefits, and additional perks included in the total compensation package.
- Equal-opportunity workplace with reasonable accommodation available during hiring and employment.
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