ML Engineer – NLP & AI Systems at Scale

Digitailor; Mountain View (CA); Presencial; USD 160.000 - 220.000; Jornada completa

Descripción de la vacante

Ema is hiring a Machine Learning Engineer to design, build, and deploy AI models that power NLP, retrieval, and dialog systems. You will work on large-scale datasets, implement Transformer-based architectures, and collaborate across teams to ship robust AI products.

You will contribute to problem definition, data exploration, model training, evaluation, and deployment. The role suits someone who thrives in a fast-paced startup and can communicate complex ML concepts to non-technical stakeholders.

Formación

• Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
• At least 2 years of industry experience in building and deploying production-level ML models.
• Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models.
• Proficiency in Python and ML libraries such as TensorFlow or PyTorch; cloud familiarity (GCP or Azure).

Responsabilidades

• Conceptualize, develop, and deploy ML models that underpin NLP, retrieval, ranking, reasoning, dialog and code-generation systems.
• Implement advanced ML algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems.
• Process and analyze large, complex datasets to inform model development and performance improvements.
• Lead end-to-end ML lifecycle from problem definition and data exploration to training, validation and deployment; communicate results to stakeholders.

Descripción del empleo

Ema is hiring a Machine Learning Engineer to design, build, and deploy AI models that power NLP, retrieval, and dialog systems. You will work on large-scale datasets, implement Transformer-based architectures, and collaborate across teams to ship robust AI products.

You will contribute to problem definition, data exploration, model training, evaluation, and deployment. The role suits someone who thrives in a fast-paced startup and can communicate complex ML concepts to non-technical stakeholders.