Machine Learning Engineer (Mid-Level)
San Francisco · hoy
Diseñarás, entrenarás, evaluarás y desplegarás modelos de aprendizaje automático en producción y pipelines de ML de extremo a extremo, trabajando con equipos de producto e ingeniería. También depurarás y mejorarás el rendimiento de los modelos usando datos de monitoreo y contribuirás a la infraestructura de ML.
- Modalidad
- Presencial
- Nivel
- Intermedio
- Experiencia
- 3+ años
- Equipo
- Engineering
Skills
Sobre el puesto
About the Role Build and deploy production machine learning systems in a fast-moving product environment. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs. What You'll Do • Design, train, and evaluate machine learning models for production use cases. • Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring. • Work with product and engineering partners to turn business requirements into ML solutions. • Debug and improve production model performance using monitoring data and real-world feedback. • Write maintainable code and contribute to ML infrastructure and tooling. • Participate in code reviews and share technical knowledge with teammates. What We're Looking For • At least 3 years of professional machine learning or software engineering experience, including hands-on ownership of production ML systems in a startup or fast-moving product environment. • Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation. • Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn. • Experience implementing production ML pipelines that cover data preprocessing, model serving, and monitoring. • Experience deploying and maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker. • Experience with production A/B testing or experimentation frameworks, and optimizing models based on results. • A completed university degree and professional employment experience after graduation. • Ability to work through ambiguity and prioritize practical impact in a dynamic environment. Location On-site in San Francisco, California.
Publicada el 8 nov 2026