Senior Machine Learning Engineer — Remote (Canada)

Yelp, Inc; Toronto; Híbrido; CAD 191.000 - 262.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Five star benefits

Descripción de la vacante

Yelp is seeking a Staff ML Engineer on the Content Contributor Intelligence team. You’ll build end-to-end ML systems, from training to production, spanning text, images, and videos with NN, LLM, and embedding techniques.

You’ll mentor engineers, handle large datasets, and deploy models for offline and online services, collaborating across teams to drive best practices in ML tech and operations.

Formación

• Experience developing and productionizing ML models, incl. neural nets, CV, and LLMs, with data pipelines.
• Proficiency with ML frameworks: PyTorch, TensorFlow, Spark MLlib, XGBoost, Sklearn.
• Strong coding skills in Python (or Java/C++).
• Solid understanding of engineering and infrastructure best practices.
• Curiosity to find new solutions and persistence to deliver.
• Experience with LLMs and LLM APIs (OpenAI, Bedrock) and prompt engineering.

Responsabilidades

• Conduct end-to-end analyses from data wrangling to statistical modeling and business framing.
• Mentor and guide junior engineers to foster learning and technical excellence.
• Work with large textual and visual datasets.
• Develop and deploy ML projects for offline batch data and real-time online services.
• Collaborate on text and visual understanding, fine-tuning transformers for embeddings.
• Productionize and automate ML pipelines in Python services.

Conocimientos

ML

Python

Mentoring

Data wrangling

SQL

Herramientas

PyTorch

TensorFlow

XGBoost

Spark MLlib

Sklearn

Descripción del empleo

Yelp is seeking a Staff ML Engineer on the Content Contributor Intelligence team. You’ll build end-to-end ML systems, from training to production, spanning text, images, and videos with NN, LLM, and embedding techniques.

You’ll mentor engineers, handle large datasets, and deploy models for offline and online services, collaborating across teams to drive best practices in ML tech and operations.