Staff Machine Learning Engineer

Accountabilities

• • Design and implement efficient, scalable machine learning pipelines supporting a range of business and insurance use cases.
  • Develop and maintain production-grade infrastructure for both classical machine learning and large language model workloads.
  • Build reusable, modular infrastructure components and CI/CD pipelines that enable rapid experimentation and reliable transitions from research to production.
  • Establish and maintain standards for scalability, reliability, security, testing, and compliance when deploying ML systems.
  • Implement observability and monitoring practices, including model and data drift detection, logging, performance tracking, and automated rollback strategies.
  • Deploy and operate machine learning solutions using cloud platforms, APIs, and cloud-based data processing and storage services.
  • Serve as a technical leader and mentor for junior engineers, providing guidance that strengthens engineering quality and team capabilities.
  • Collaborate with cross-functional teams to translate complex technical challenges into practical, production-ready solutions.
  • Balance rapid innovation with operational stability, ensuring ML systems remain reliable and maintainable as they scale.

  Requirements

  • 8+ years of professional experience as a Machine Learning Engineer or in a closely related engineering role.
  • At least 2 years of hands-on experience working extensively with large language models and LLM-based applications.
  • Strong expertise in designing robust and scalable machine learning pipelines for both traditional ML and LLM workloads.
  • Demonstrated experience automating, deploying, monitoring, and maintaining machine learning workflows in production environments.
  • Hands-on experience with cloud platforms, including model deployment, cloud resource management, and APIs for data ingestion, storage, and processing.
  • Strong understanding of production ML engineering practices, including scalability, reliability, observability, testing, monitoring, and CI/CD.
  • Ability to provide technical leadership, mentor engineers, and establish effective engineering standards.
  • Strong communication skills, with the ability to explain sophisticated technical concepts clearly to non-technical stakeholders.
  • A first-principles mindset, strong problem-solving abilities, adaptability, and enthusiasm for learning and experimentation.
  • Ability to work effectively in a fast-paced environment where priorities evolve and rapid iteration is encouraged.

  Benefits

  • Annual cash compensation of $210,000–$250,000, with final compensation determined by factors such as location, experience, and expertise.
  • Stock options as part of the total compensation package.
  • Comprehensive employee benefits and additional perks.
  • Opportunity to work on advanced AI and machine learning challenges with significant real-world impact.
  • Exposure to complex LLM and ML systems across an AI-native technology environment.
  • Strong opportunities for technical leadership, mentorship, and professional growth.
  • A collaborative culture that values first-principles thinking, continuous learning, experimentation, and adaptability.
  • Remote work environment with the opportunity to contribute to a rapidly growing global organization.
  • Inclusive workplace committed to equal opportunity and providing reasonable accommodations throughout the employment process.