Remote Senior ML Engineer - Build Production-Ready AI Systems

Kake Group; United States; A distancia; USD 150.000 - 230.000; Jornada completa

Descripción de la vacante

Kake is seeking an experienced Senior Machine Learning Engineer for a fully remote role in the United States. You will design, build, and deploy production-grade ML systems, owning architectures and experimentation end-to-end, with strong Python and modern ML framework expertise.

You will lead pipelines, monitor performance, and mentor peers while collaborating with stakeholders to align ML work with business goals.

Formación

• 5+ years in ML engineering with production deployments.
• Expert-level Python with PyTorch or TensorFlow.
• Strong ML fundamentals and scaling techniques for production.
• Experience building ML infrastructure on cloud (AWS/GCP/Azure).
• Strong software engineering skills including CI/CD and maintainable code.
• SQL, distributed data processing (e.g., Spark), and large datasets.
• Excellent written and verbal English.
• Ability to work independently in a remote, fast-paced environment.

Responsabilidades

• Lead end-to-end design, development, and deployment of scalable ML systems in production.
• Architect robust ML pipelines and tooling choices.
• Translate business goals into technical roadmaps for ML solutions.
• Own model performance, reliability, and monitoring in production.
• Mentor engineers through code reviews and feedback.
• Drive experimentation strategy and evaluate new techniques and tools.
• Communicate technical trade-offs and results to diverse audiences.

Conocimientos

Python

PyTorch

TensorFlow

ML systems

CI/CD

SQL

Herramientas

Docker

Kubernetes

MLflow

Weights & Biases

Descripción del empleo

Kake is seeking an experienced Senior Machine Learning Engineer for a fully remote role in the United States. You will design, build, and deploy production-grade ML systems, owning architectures and experimentation end-to-end, with strong Python and modern ML framework expertise.

You will lead pipelines, monitor performance, and mentor peers while collaborating with stakeholders to align ML work with business goals.