Machine Learning Engineer

Wearestateside; California (MO); Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative digital agency is seeking a talented Machine Learning Engineer to join their dynamic AI and data science team. This remote role involves designing, developing, and deploying machine learning models that drive impactful data-driven solutions. You will collaborate with cross-functional teams, optimizing model performance and ensuring the delivery of intelligent systems at scale. If you are passionate about leveraging machine learning to solve complex problems and thrive in a collaborative environment, this opportunity is perfect for you.

Qualifications

• 3-5+ years of hands-on experience with machine learning models in production.
• Proficiency in Python and ML frameworks like TensorFlow and PyTorch.

Responsibilities

• Design and implement machine learning models for various tasks.
• Collaborate with data engineers to build efficient data ingestion systems.

Skills

Machine Learning

Python

scikit-learn

TensorFlow

PyTorch

Statistics

Data Structures

Algorithms

Version Control (Git)

Cloud Services (AWS, GCP, Azure)

Education

Bachelor's in Computer Science

Master's in Data Science

Ph.D. in related field

Tools

MLflow

Airflow

Kubeflow

Docker

CI/CD

Job description

Stateside is a minority-owned, California, Small-Business Certified creative & technical digital agency that provides efficient, scalable production services or teams through co-location of resources in the U.S. and LATAM.

Job Description

This is a remote position.

Machine Learning Engineer

Position Title: Machine Learning Engineer

Location: Remote

Department: Data Science / Engineering

Employment Type: Full-Time

About the Role

We are looking for a highly skilledMachine Learning Engineerto join our AI and data science team. In this role, you will design, develop, and deploy machine learning models and pipelines that power critical data-driven solutions across our organization. You’ll collaborate with data scientists, software engineers, and product teams to deliver intelligent systems at scale.

Responsibilities

Design and implement machine learning models for classification, regression, recommendation, NLP, or time-series forecasting tasks.

Develop, test, and maintain scalable ML pipelines for training, validation, and inference.

Collaborate with data engineers to build efficient data ingestion and feature extraction systems.

Optimize model performance using techniques like hyperparameter tuning, cross-validation, and regularization.

Deploy models to production using MLOps practices with tools like MLflow, TFX, or SageMaker.

Monitor and maintain the health of deployed models, updating them as needed.

Document ML experiments, metrics, and decisions.

Work closely with cross-functional teams to identify machine learning opportunities and define technical solutions.

Requirements

Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related field (Ph.D. a plus).

3–5+ years of hands-on experience building machine learning models in production.

Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.

Experience with ML pipeline tools (e.g., Airflow, Kubeflow, MLflow).

Familiarity with cloud services (AWS, GCP, or Azure) and model deployment.

Solid understanding of statistics, data structures, and algorithms.

Experience with version control (Git), containerization (Docker), and CI/CD for ML.

Preferred Qualifications

Experience with NLP or computer vision projects.

Familiarity with big data tools (e.g., Spark, Hadoop).

Experience using GPU-accelerated training environments.

Requirements

Requirements

Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related field (Ph.D. a plus).

3–5+ years of hands-on experience building machine learning models in production.

Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.

Experience with ML pipeline tools (e.g., Airflow, Kubeflow, MLflow).

Familiarity with cloud services (AWS, GCP, or Azure) and model deployment.

Solid understanding of statistics, data structures, and algorithms.

Experience with version control (Git), containerization (Docker), and CI/CD for ML.

Preferred Qualifications

Experience with NLP or computer vision projects.

Familiarity with big data tools (e.g., Spark, Hadoop).

Experience using GPU-accelerated training environments.