Mid-Level Machine Learning Engineer

Wwshemi; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Wwshemi is seeking an experienced ML Engineer to design, train, and deploy production-ready machine learning models. You will build end-to-end pipelines, monitor models, and collaborate across product and engineering teams to translate business needs into practical ML solutions.

The role requires hands-on experience with ML systems, Python and frameworks like TensorFlow or PyTorch, plus knowledge of MLOps and cloud platforms.

Qualifications

• A completed university degree and at least 3 years of professional experience in machine learning or software engineering, including post-graduation employment.
• Hands-on experience building and deploying production ML systems in an ML engineering role at a startup or fast-moving product company.
• Strong understanding of model selection, evaluation metrics, feature engineering, and validation techniques.
• Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
• Experience implementing and maintaining production ML pipelines, including preprocessing, model serving, and monitoring at scale.
• Experience with MLOps tools or cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
• Experience with production A/B testing or experimentation, and optimizing models based on observed performance.
• Comfort working through ambiguity and iterating quickly in a product environment.

Responsibilities

• Design, train, and evaluate machine learning models for production use cases.
• Build end-to-end ML pipelines spanning data preprocessing, model serving, and monitoring.
• Work with product and engineering partners to turn business needs 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 knowledge with teammates.

Skills

3+ years experience

Python proficiency

ML lifecycle

Problem solving

Team collaboration

Communication

Education

Bachelor's degree in CS/related field

Tools

TensorFlow

PyTorch

scikit-learn

AWS SageMaker

GCP Vertex AI

Kubernetes

Docker

Job description

About the Role

Build and deploy machine learning systems for a fast-moving technology product team. You will own work across the ML lifecycle, from defining problems to monitoring models in production, partnering with product, engineering, and domain experts to deliver useful outcomes.

What You'll Do

• Design, train, and evaluate machine learning models for production use cases.
• Build end-to-end ML pipelines spanning data preprocessing, model serving, and monitoring.
• Work with product and engineering partners to turn business needs 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 knowledge with teammates.

What We're Looking For

• A completed university degree and at least 3 years of professional experience in machine learning or software engineering, including post-graduation employment.
• Hands-on experience building and deploying production ML systems in an ML engineering role at a startup or fast-moving product company.
• Strong understanding of model selection, evaluation metrics, feature engineering, and validation techniques.
• Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
• Experience implementing and maintaining production ML pipelines, including preprocessing, model serving, and monitoring at scale.
• Experience with MLOps tools or cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
• Experience with production A/B testing or experimentation, and optimizing models based on observed performance.
• Comfort working through ambiguity and iterating quickly in a product environment.

Location

On-site in San Francisco, California, United States.