Mid-Level Machine Learning Engineer
At a glance
Mid-level Machine Learning Engineering role at Clera. San Francisco · full-time · no salary stated · we estimate $185,750–$272,875.
Clera has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Train and assess models for live product applications, preparing them for use in production
- Create ML workflows that handle data preparation, model delivery and ongoing performance checks
- Turn product and engineering needs into machine learning solutions with your partners
- Use monitoring results and user feedback to diagnose and improve deployed models
- Write maintainable code, strengthen ML tools and infrastructure, and take part in code reviews
What you bring
- A completed university degree and at least three years of post-graduation work in machine learning or software engineering
- Practical experience developing and launching ML systems for real-world use as an ML engineer at a startup or fast-paced product company
- A solid grasp of choosing models, measuring results, engineering features and validating approaches
- Python skills and hands-on use of TensorFlow, PyTorch or scikit-learn
- Experience maintaining production ML workflows and using MLOps or cloud tools such as AWS SageMaker, GCP Vertex AI or another listed tool
Who this fits
This role suits a mid-level engineer who has delivered ML systems for production and is comfortable adapting to unclear problems. The position is on-site in San Francisco.
From the employer
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.
Machine Learning Engineering work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Machine Learning Engineering
Find me workBy the team behind aimlroles.